Impact of Interstock and Rootstock on the Vigor and Productive Development of Mango (Mangifera indica L.) in the San Lorenzo Valley, Piura, Peru

1 Project Setup

Install inti development version.

if (!require("remotes"))
  install.packages("remotes")
remotes::install_github("flavjack/inti")
library(emmeans)
library(corrplot)
library(multcomp)
library(FSA)
library(factoextra)
library(corrplot)
source('https://inkaverse.com/setup.r')

cat("Project: ", getwd(), "\n")
Project:  C:/INIA/GIT/prochira_injertos 
session_info()
─ Session info ───────────────────────────────────────────────────────────────
 setting  value
 version  R version 4.4.1 (2024-06-14 ucrt)
 os       Windows 11 x64 (build 22631)
 system   x86_64, mingw32
 ui       RTerm
 language (EN)
 collate  Spanish_Peru.utf8
 ctype    Spanish_Peru.utf8
 tz       America/Lima
 date     2024-10-01
 pandoc   3.2 @ C:/Program Files/RStudio/resources/app/bin/quarto/bin/tools/ (via rmarkdown)

─ Packages ───────────────────────────────────────────────────────────────────
 package       * version  date (UTC) lib source
 agricolae       1.3-7    2023-10-22 [1] CRAN (R 4.4.0)
 AlgDesign       1.2.1    2022-05-25 [1] CRAN (R 4.4.0)
 askpass         1.2.0    2023-09-03 [1] CRAN (R 4.4.0)
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 corrplot      * 0.92     2021-11-18 [1] CRAN (R 4.4.0)
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 digest          0.6.35   2024-03-11 [1] CRAN (R 4.4.0)
 dplyr         * 1.1.4    2023-11-17 [1] CRAN (R 4.4.0)
 DT              0.33     2024-04-04 [1] CRAN (R 4.4.0)
 ellipsis        0.3.2    2021-04-29 [1] CRAN (R 4.4.0)
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 estimability    1.5.1    2024-05-12 [1] CRAN (R 4.4.0)
 evaluate        0.23     2023-11-01 [1] CRAN (R 4.4.0)
 factoextra    * 1.0.7    2020-04-01 [1] CRAN (R 4.4.0)
 FactoMineR    * 2.11     2024-04-20 [1] CRAN (R 4.4.0)
 fansi           1.0.6    2023-12-08 [1] CRAN (R 4.4.0)
 fastmap         1.2.0    2024-05-15 [1] CRAN (R 4.4.0)
 flashClust      1.01-2   2012-08-21 [1] CRAN (R 4.4.0)
 forcats       * 1.0.0    2023-01-29 [1] CRAN (R 4.4.0)
 fs              1.6.4    2024-04-25 [1] CRAN (R 4.4.0)
 FSA           * 0.9.5    2023-08-26 [1] CRAN (R 4.4.0)
 gargle          1.5.2    2023-07-20 [1] CRAN (R 4.4.0)
 generics        0.1.3    2022-07-05 [1] CRAN (R 4.4.0)
 ggplot2       * 3.5.1    2024-04-23 [1] CRAN (R 4.4.0)
 ggrepel         0.9.5    2024-01-10 [1] CRAN (R 4.4.0)
 glue            1.7.0    2024-01-09 [1] CRAN (R 4.4.0)
 googledrive   * 2.1.1    2023-06-11 [1] CRAN (R 4.4.0)
 googlesheets4 * 1.1.1    2023-06-11 [1] CRAN (R 4.4.0)
 gsheet        * 0.4.5    2020-04-07 [1] CRAN (R 4.4.0)
 gtable          0.3.5    2024-04-22 [1] CRAN (R 4.4.0)
 hms             1.1.3    2023-03-21 [1] CRAN (R 4.4.0)
 htmltools       0.5.8.1  2024-04-04 [1] CRAN (R 4.4.0)
 htmlwidgets     1.6.4    2023-12-06 [1] CRAN (R 4.4.0)
 httpuv          1.6.15   2024-03-26 [1] CRAN (R 4.4.0)
 httr            1.4.7    2023-08-15 [1] CRAN (R 4.4.0)
 huito         * 0.2.4    2023-10-25 [1] CRAN (R 4.4.0)
 inti          * 0.6.5    2024-08-02 [1] Github (flavjack/inti@38be898)
 jsonlite        1.8.8    2023-12-04 [1] CRAN (R 4.4.0)
 knitr         * 1.46     2024-04-06 [1] CRAN (R 4.4.0)
 later           1.3.2    2023-12-06 [1] CRAN (R 4.4.0)
 lattice         0.22-6   2024-03-20 [2] CRAN (R 4.4.1)
 leaps           3.1      2020-01-16 [1] CRAN (R 4.4.0)
 lifecycle       1.0.4    2023-11-07 [1] CRAN (R 4.4.0)
 lme4            1.1-35.3 2024-04-16 [1] CRAN (R 4.4.0)
 lubridate     * 1.9.3    2023-09-27 [1] CRAN (R 4.4.0)
 magick        * 2.8.3    2024-02-18 [1] CRAN (R 4.4.0)
 magrittr        2.0.3    2022-03-30 [1] CRAN (R 4.4.0)
 MASS          * 7.3-60.2 2024-04-26 [2] CRAN (R 4.4.1)
 Matrix          1.7-0    2024-04-26 [2] CRAN (R 4.4.1)
 memoise         2.0.1    2021-11-26 [1] CRAN (R 4.4.0)
 mime            0.12     2021-09-28 [1] CRAN (R 4.4.0)
 miniUI          0.1.1.1  2018-05-18 [1] CRAN (R 4.4.0)
 minqa           1.2.7    2024-05-20 [1] CRAN (R 4.4.0)
 mnormt          2.1.1    2022-09-26 [1] CRAN (R 4.4.0)
 multcomp      * 1.4-25   2023-06-20 [1] CRAN (R 4.4.0)
 multcompView    0.1-10   2024-03-08 [1] CRAN (R 4.4.0)
 munsell         0.5.1    2024-04-01 [1] CRAN (R 4.4.0)
 mvtnorm       * 1.2-5    2024-05-21 [1] CRAN (R 4.4.0)
 nlme            3.1-164  2023-11-27 [2] CRAN (R 4.4.1)
 nloptr          2.0.3    2022-05-26 [1] CRAN (R 4.4.0)
 openssl         2.2.0    2024-05-16 [1] CRAN (R 4.4.0)
 pillar          1.9.0    2023-03-22 [1] CRAN (R 4.4.0)
 pkgbuild        1.4.4    2024-03-17 [1] CRAN (R 4.4.0)
 pkgconfig       2.0.3    2019-09-22 [1] CRAN (R 4.4.0)
 pkgload         1.3.4    2024-01-16 [1] CRAN (R 4.4.0)
 profvis         0.3.8    2023-05-02 [1] CRAN (R 4.4.0)
 promises        1.3.0    2024-04-05 [1] CRAN (R 4.4.0)
 psych         * 2.4.3    2024-03-18 [1] CRAN (R 4.4.0)
 purrr         * 1.0.2    2023-08-10 [1] CRAN (R 4.4.0)
 R6              2.5.1    2021-08-19 [1] CRAN (R 4.4.0)
 rappdirs        0.3.3    2021-01-31 [1] CRAN (R 4.4.0)
 Rcpp            1.0.12   2024-01-09 [1] CRAN (R 4.4.0)
 readr         * 2.1.5    2024-01-10 [1] CRAN (R 4.4.0)
 remotes         2.5.0    2024-03-17 [1] CRAN (R 4.4.0)
 rlang           1.1.3    2024-01-10 [1] CRAN (R 4.4.0)
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 scatterplot3d   0.3-44   2023-05-05 [1] CRAN (R 4.4.0)
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 shiny         * 1.8.1.1  2024-04-02 [1] CRAN (R 4.4.0)
 showtext        0.9-7    2024-03-02 [1] CRAN (R 4.4.0)
 showtextdb      3.0      2020-06-04 [1] CRAN (R 4.4.0)
 stringi         1.8.4    2024-05-06 [1] CRAN (R 4.4.0)
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 TH.data       * 1.1-2    2023-04-17 [1] CRAN (R 4.4.0)
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 tidyr         * 1.3.1    2024-01-24 [1] CRAN (R 4.4.0)
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 vctrs           0.6.5    2023-12-01 [1] CRAN (R 4.4.0)
 withr           3.0.1    2024-07-31 [1] CRAN (R 4.4.1)
 xfun            0.44     2024-05-15 [1] CRAN (R 4.4.0)
 xtable          1.8-4    2019-04-21 [1] CRAN (R 4.4.0)
 yaml            2.3.8    2023-12-11 [1] CRAN (R 4.4.0)
 zoo             1.8-12   2023-04-13 [1] CRAN (R 4.4.0)

 [1] C:/Users/INIA/AppData/Local/R/win-library/4.4
 [2] C:/Program Files/R/R-4.4.1/library

──────────────────────────────────────────────────────────────────────────────

2 Import data

Data were imported from the field book evaluated during the 2017-2019 growing seasons. The evaluations focused on the agronomic traits and fruit biometrics of the mango crop.

url <- "https://docs.google.com/spreadsheets/d/1E_cUmiRFoPLGAj7AQC6HZBD6hXtqzg2UHPvtWr2QR_U/edit#gid=2112492836"

gs <- url %>% 
  as_sheets_id()

ley <- gs %>% 
  range_read("leyenda") %>% 
  rename(tratamientos = TRATAM) %>% 
  rename_with(~ tolower(.))

rdt <- gs %>% 
  range_read("db") %>% 
  merge(., ley) %>% 
  dplyr::select(year = "año", n, tratamientos,n_trat:yema, everything()) %>% 
  rename(treat = tratamientos
         , n_treat = n_trat
         , block = bloque
         , n_plant = n_planta
         , height = alt_planta
         , n_fruits = n_frutos
         , flowering = per_floracion
         , sproud = per_brote
         , scion = yema
         , stock = patron
         , edge = puente
         ) %>% 
  dplyr::arrange(year, n, treat) %>% 
  mutate(across(year:n_plant, ~ as.factor(.))) 

glimpse(rdt)
## Rows: 648
## Columns: 13
## $ year      <fct> 2017, 2017, 2017, 2017, 2017, 2017, 2017, 2017, 2017, 2017, …
## $ n         <fct> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 1…
## $ treat     <fct> 141, 141, 141, 141, 141, 141, 141, 141, 141, 231, 231, 231, …
## $ n_treat   <fct> 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 8, 8, …
## $ stock     <fct> CHULUCANAS, CHULUCANAS, CHULUCANAS, CHULUCANAS, CHULUCANAS, …
## $ edge      <fct> CHULUCANAS, CHULUCANAS, CHULUCANAS, CHULUCANAS, CHULUCANAS, …
## $ scion     <fct> KENT, KENT, KENT, KENT, KENT, KENT, KENT, KENT, KENT, KENT, …
## $ block     <fct> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, …
## $ n_plant   <fct> 1, 2, 3, 4, 5, 6, 7, 8, 9, 1, 2, 3, 4, 5, 6, 7, 8, 9, 1, 2, …
## $ height    <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
## $ n_fruits  <dbl> 170, 200, 310, NA, 235, 185, 180, 132, 80, 231, 198, 195, 20…
## $ flowering <dbl> 60, 50, 50, NA, 80, 60, 40, 90, 90, 60, 70, 40, 90, 80, 80, …
## $ sproud    <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
  
fru <-  gs %>% 
  range_read("db_frutos") %>% 
  merge(., ley, ) %>% 
  dplyr::select(year = "año", n, tratamientos,n_trat:yema, everything()) %>% 
  rename(treat = tratamientos
         , n_treat = n_trat
         , block = bloque
         , n_plant = n_planta
         , weigth = peso
         , long = largo
         , n_fruits = n_frutos
         , diameter_1 = diametro_1
         , diameter_2 = diametro_2
         , sample = muestra
         , scion = yema
         , stock = patron
         , edge = puente
         ) %>% 
  dplyr::arrange(year, n, treat) %>% 
  mutate(diameter_average = (diameter_1 + diameter_2)/2) %>% 
  mutate(across(year:sample, ~ as.factor(.)))

glimpse(fru)
## Rows: 240
## Columns: 16
## $ year             <fct> 2023, 2023, 2023, 2023, 2023, 2023, 2023, 2023, 2023,…
## $ n                <fct> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16…
## $ treat            <fct> 141, 141, 141, 141, 141, 141, 141, 141, 141, 141, 231…
## $ n_treat          <fct> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2,…
## $ stock            <fct> CHULUCANAS, CHULUCANAS, CHULUCANAS, CHULUCANAS, CHULU…
## $ edge             <fct> CHULUCANAS, CHULUCANAS, CHULUCANAS, CHULUCANAS, CHULU…
## $ scion            <fct> KENT, KENT, KENT, KENT, KENT, KENT, KENT, KENT, KENT,…
## $ block            <fct> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,…
## $ n_plant          <fct> 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 2, 2, 2,…
## $ sample           <fct> 1, 2, 3, 4, 5, 1, 2, 3, 4, 5, 1, 2, 3, 4, 5, 1, 2, 3,…
## $ n_fruits         <dbl> 26, 26, 26, 26, 26, 35, 35, 35, 35, 35, 118, 118, 118…
## $ weigth           <dbl> 270, 520, 385, 230, 285, 600, 457, 520, 665, 305, 422…
## $ long             <dbl> 86, 111, 103, 86, 88, 116, 101, 115, 126, 91, 100, 10…
## $ diameter_1       <dbl> 78, 90, 86, 70, 78, 96, 88, 88, 103, 78, 91, 84, 84, …
## $ diameter_2       <dbl> 74, 83, 75, 69, 71, 87, 88, 81, 95, 80, 83, 76, 72, 8…
## $ diameter_average <dbl> 76.0, 86.5, 80.5, 69.5, 74.5, 91.5, 88.0, 84.5, 99.0,…
ley %>% kable(caption = "Interstock grafting treatments", align = 'c')
Interstock grafting treatments
n_trat tratamientos patron puente yema
1 141 CHULUCANAS CHULUCANAS KENT
2 231 CHATO JULIE KENT
3 241 CHULUCANAS CHATO KENT
4 211 CHATO IRWIN KENT
5 131 CHULUCANAS JULIE KENT
6 111 CHULUCANAS IRWIN KENT
7 221 CHATO CHATO KENT
8 121 CHATO CHULUCANAS KENT 

rdt %>% kable(caption = "Evaluation of the agronomic characteristics of mango", align = 'c')
Evaluation of the agronomic characteristics of mango
year n treat n_treat stock edge scion block n_plant height n_fruits flowering sproud
2017 1 141 1 CHULUCANAS CHULUCANAS KENT 1 1 170 60
2017 2 141 1 CHULUCANAS CHULUCANAS KENT 1 2 200 50
2017 3 141 1 CHULUCANAS CHULUCANAS KENT 1 3 310 50
2017 4 141 1 CHULUCANAS CHULUCANAS KENT 1 4
2017 5 141 1 CHULUCANAS CHULUCANAS KENT 1 5 235 80
2017 6 141 1 CHULUCANAS CHULUCANAS KENT 1 6 185 60
2017 7 141 1 CHULUCANAS CHULUCANAS KENT 1 7 180 40
2017 8 141 1 CHULUCANAS CHULUCANAS KENT 1 8 132 90
2017 9 141 1 CHULUCANAS CHULUCANAS KENT 1 9 80 90
2017 10 231 2 CHATO JULIE KENT 1 1 231 60
2017 11 231 2 CHATO JULIE KENT 1 2 198 70
2017 12 231 2 CHATO JULIE KENT 1 3 195 40
2017 13 231 2 CHATO JULIE KENT 1 4 200 90
2017 14 231 2 CHATO JULIE KENT 1 5 180 80
2017 15 231 2 CHATO JULIE KENT 1 6 140 80
2017 16 231 2 CHATO JULIE KENT 1 7 200 60
2017 17 231 2 CHATO JULIE KENT 1 8 186 75
2017 18 231 2 CHATO JULIE KENT 1 9
2017 19 121 8 CHATO CHULUCANAS KENT 1 1
2017 20 121 8 CHATO CHULUCANAS KENT 1 2 165 75
2017 21 121 8 CHATO CHULUCANAS KENT 1 3 210 75
2017 22 121 8 CHATO CHULUCANAS KENT 1 4 201 80
2017 23 121 8 CHATO CHULUCANAS KENT 1 5 110 80
2017 24 121 8 CHATO CHULUCANAS KENT 1 6 215 90
2017 25 121 8 CHATO CHULUCANAS KENT 1 7 200 80
2017 26 121 8 CHATO CHULUCANAS KENT 1 8 160 80
2017 27 121 8 CHATO CHULUCANAS KENT 1 9 162 75
2017 28 211 4 CHATO IRWIN KENT 1 1 204 70
2017 29 211 4 CHATO IRWIN KENT 1 2 124 80
2017 30 211 4 CHATO IRWIN KENT 1 3 130 80
2017 31 211 4 CHATO IRWIN KENT 1 4 6 5
2017 32 211 4 CHATO IRWIN KENT 1 5 90 35
2017 33 211 4 CHATO IRWIN KENT 1 6 170 60
2017 34 211 4 CHATO IRWIN KENT 1 7 165 45
2017 35 211 4 CHATO IRWIN KENT 1 8 100 25
2017 36 211 4 CHATO IRWIN KENT 1 9 70 25
2017 37 131 5 CHULUCANAS JULIE KENT 1 1 75 80
2017 38 131 5 CHULUCANAS JULIE KENT 1 2 200 80
2017 39 131 5 CHULUCANAS JULIE KENT 1 3 184 80
2017 40 131 5 CHULUCANAS JULIE KENT 1 4 102 70
2017 41 131 5 CHULUCANAS JULIE KENT 1 5 190 60
2017 42 131 5 CHULUCANAS JULIE KENT 1 6 180 70
2017 43 131 5 CHULUCANAS JULIE KENT 1 7 175 75
2017 44 131 5 CHULUCANAS JULIE KENT 1 8 120 80
2017 45 131 5 CHULUCANAS JULIE KENT 1 9 155 60
2017 46 241 3 CHULUCANAS CHATO KENT 1 1 230 90
2017 47 241 3 CHULUCANAS CHATO KENT 1 2 110 90
2017 48 241 3 CHULUCANAS CHATO KENT 1 3 185 90
2017 49 241 3 CHULUCANAS CHATO KENT 1 4 220 90
2017 50 241 3 CHULUCANAS CHATO KENT 1 5 180 90
2017 51 241 3 CHULUCANAS CHATO KENT 1 6 175 60
2017 52 241 3 CHULUCANAS CHATO KENT 1 7 210 90
2017 53 241 3 CHULUCANAS CHATO KENT 1 8 180 90
2017 54 241 3 CHULUCANAS CHATO KENT 1 9 178 90
2017 55 111 6 CHULUCANAS IRWIN KENT 1 1 150 90
2017 56 111 6 CHULUCANAS IRWIN KENT 1 2 160 85
2017 57 111 6 CHULUCANAS IRWIN KENT 1 3 200 90
2017 58 111 6 CHULUCANAS IRWIN KENT 1 4 140 90
2017 59 111 6 CHULUCANAS IRWIN KENT 1 5 175 90
2017 60 111 6 CHULUCANAS IRWIN KENT 1 6 200 80
2017 61 111 6 CHULUCANAS IRWIN KENT 1 7 208 90
2017 62 111 6 CHULUCANAS IRWIN KENT 1 8 300 90
2017 63 111 6 CHULUCANAS IRWIN KENT 1 9 230 90
2017 64 221 7 CHATO CHATO KENT 1 1 190 90
2017 65 221 7 CHATO CHATO KENT 1 2 206 90
2017 66 221 7 CHATO CHATO KENT 1 3 145 80
2017 67 221 7 CHATO CHATO KENT 1 4 51 20
2017 68 221 7 CHATO CHATO KENT 1 5 45 30
2017 69 221 7 CHATO CHATO KENT 1 6 163 80
2017 70 221 7 CHATO CHATO KENT 1 7 35 15
2017 71 221 7 CHATO CHATO KENT 1 8 200 80
2017 72 221 7 CHATO CHATO KENT 1 9 32 15
2017 73 211 4 CHATO IRWIN KENT 2 1 170 90
2017 74 211 4 CHATO IRWIN KENT 2 2 350 80
2017 75 211 4 CHATO IRWIN KENT 2 3 200 90
2017 76 211 4 CHATO IRWIN KENT 2 4 154 70
2017 77 211 4 CHATO IRWIN KENT 2 5 180 90
2017 78 211 4 CHATO IRWIN KENT 2 6 130 90
2017 79 211 4 CHATO IRWIN KENT 2 7 145 90
2017 80 211 4 CHATO IRWIN KENT 2 8 130 90
2017 81 211 4 CHATO IRWIN KENT 2 9 220 90
2017 82 121 8 CHATO CHULUCANAS KENT 2 1 223 90
2017 83 121 8 CHATO CHULUCANAS KENT 2 2 220 90
2017 84 121 8 CHATO CHULUCANAS KENT 2 3 230 90
2017 85 121 8 CHATO CHULUCANAS KENT 2 4 200 90
2017 86 121 8 CHATO CHULUCANAS KENT 2 5 210 90
2017 87 121 8 CHATO CHULUCANAS KENT 2 6 150 90
2017 88 121 8 CHATO CHULUCANAS KENT 2 7 220 80
2017 89 121 8 CHATO CHULUCANAS KENT 2 8 270 90
2017 90 121 8 CHATO CHULUCANAS KENT 2 9 170 85
2017 91 231 2 CHATO JULIE KENT 2 1 28 90
2017 92 231 2 CHATO JULIE KENT 2 2 250 90
2017 93 231 2 CHATO JULIE KENT 2 3 115 70
2017 94 231 2 CHATO JULIE KENT 2 4 160 80
2017 95 231 2 CHATO JULIE KENT 2 5 200 90
2017 96 231 2 CHATO JULIE KENT 2 6 150 90
2017 97 231 2 CHATO JULIE KENT 2 7 120 90
2017 98 231 2 CHATO JULIE KENT 2 8 230 90
2017 99 231 2 CHATO JULIE KENT 2 9 240 80
2017 100 141 1 CHULUCANAS CHULUCANAS KENT 2 1 210 80
2017 101 141 1 CHULUCANAS CHULUCANAS KENT 2 2 205 90
2017 102 141 1 CHULUCANAS CHULUCANAS KENT 2 3 210 90
2017 103 141 1 CHULUCANAS CHULUCANAS KENT 2 4 170 80
2017 104 141 1 CHULUCANAS CHULUCANAS KENT 2 5 120 80
2017 105 141 1 CHULUCANAS CHULUCANAS KENT 2 6 240 90
2017 106 141 1 CHULUCANAS CHULUCANAS KENT 2 7 280 90
2017 107 141 1 CHULUCANAS CHULUCANAS KENT 2 8 260 90
2017 108 141 1 CHULUCANAS CHULUCANAS KENT 2 9 170 70
2017 109 221 7 CHATO CHATO KENT 2 1 142 90
2017 110 221 7 CHATO CHATO KENT 2 2 120 90
2017 111 221 7 CHATO CHATO KENT 2 3 140 90
2017 112 221 7 CHATO CHATO KENT 2 4 180 90
2017 113 221 7 CHATO CHATO KENT 2 5 160 90
2017 114 221 7 CHATO CHATO KENT 2 6 130 90
2017 115 221 7 CHATO CHATO KENT 2 7 70 90
2017 116 221 7 CHATO CHATO KENT 2 8 115 90
2017 117 221 7 CHATO CHATO KENT 2 9 104 90
2017 118 111 6 CHULUCANAS IRWIN KENT 2 1
2017 119 111 6 CHULUCANAS IRWIN KENT 2 2 208 90
2017 120 111 6 CHULUCANAS IRWIN KENT 2 3 110 90
2017 121 111 6 CHULUCANAS IRWIN KENT 2 4 140 90
2017 122 111 6 CHULUCANAS IRWIN KENT 2 5 108 80
2017 123 111 6 CHULUCANAS IRWIN KENT 2 6 120 90
2017 124 111 6 CHULUCANAS IRWIN KENT 2 7 250 90
2017 125 111 6 CHULUCANAS IRWIN KENT 2 8 220 90
2017 126 111 6 CHULUCANAS IRWIN KENT 2 9 235 80
2017 127 241 3 CHULUCANAS CHATO KENT 2 1 170 80
2017 128 241 3 CHULUCANAS CHATO KENT 2 2 182 80
2017 129 241 3 CHULUCANAS CHATO KENT 2 3 148 90
2017 130 241 3 CHULUCANAS CHATO KENT 2 4 160 90
2017 131 241 3 CHULUCANAS CHATO KENT 2 5 23 25
2017 132 241 3 CHULUCANAS CHATO KENT 2 6 175 80
2017 133 241 3 CHULUCANAS CHATO KENT 2 7 227 90
2017 134 241 3 CHULUCANAS CHATO KENT 2 8 180 90
2017 135 241 3 CHULUCANAS CHATO KENT 2 9 50 90
2017 136 131 5 CHULUCANAS JULIE KENT 2 1 180 80
2017 137 131 5 CHULUCANAS JULIE KENT 2 2 240 90
2017 138 131 5 CHULUCANAS JULIE KENT 2 3 124 70
2017 139 131 5 CHULUCANAS JULIE KENT 2 4 220 85
2017 140 131 5 CHULUCANAS JULIE KENT 2 5 240 80
2017 141 131 5 CHULUCANAS JULIE KENT 2 6 90 80
2017 142 131 5 CHULUCANAS JULIE KENT 2 7 220 85
2017 143 131 5 CHULUCANAS JULIE KENT 2 8 250 90
2017 144 131 5 CHULUCANAS JULIE KENT 2 9 174 90
2017 145 121 8 CHATO CHULUCANAS KENT 3 1 170 85
2017 146 121 8 CHATO CHULUCANAS KENT 3 2 198 80
2017 147 121 8 CHATO CHULUCANAS KENT 3 3 125 90
2017 148 121 8 CHATO CHULUCANAS KENT 3 4 124 90
2017 149 121 8 CHATO CHULUCANAS KENT 3 5 120 90
2017 150 121 8 CHATO CHULUCANAS KENT 3 6 135 80
2017 151 121 8 CHATO CHULUCANAS KENT 3 7 150 90
2017 152 121 8 CHATO CHULUCANAS KENT 3 8 330 90
2017 153 121 8 CHATO CHULUCANAS KENT 3 9 201 80
2017 154 211 4 CHATO IRWIN KENT 3 1 210 90
2017 155 211 4 CHATO IRWIN KENT 3 2 100 90
2017 156 211 4 CHATO IRWIN KENT 3 3 160 90
2017 157 211 4 CHATO IRWIN KENT 3 4 260 90
2017 158 211 4 CHATO IRWIN KENT 3 5
2017 159 211 4 CHATO IRWIN KENT 3 6 160 85
2017 160 211 4 CHATO IRWIN KENT 3 7 210 80
2017 161 211 4 CHATO IRWIN KENT 3 8 200 85
2017 162 211 4 CHATO IRWIN KENT 3 9 225 90
2017 163 141 1 CHULUCANAS CHULUCANAS KENT 3 1 300 90
2017 164 141 1 CHULUCANAS CHULUCANAS KENT 3 2 195 70
2017 165 141 1 CHULUCANAS CHULUCANAS KENT 3 3 210 80
2017 166 141 1 CHULUCANAS CHULUCANAS KENT 3 4 230 80
2017 167 141 1 CHULUCANAS CHULUCANAS KENT 3 5 205 90
2017 168 141 1 CHULUCANAS CHULUCANAS KENT 3 6 204 80
2017 169 141 1 CHULUCANAS CHULUCANAS KENT 3 7 90 90
2017 170 141 1 CHULUCANAS CHULUCANAS KENT 3 8 180 70
2017 171 141 1 CHULUCANAS CHULUCANAS KENT 3 9 37 20
2017 172 231 2 CHATO JULIE KENT 3 1 305 90
2017 173 231 2 CHATO JULIE KENT 3 2 120 90
2017 174 231 2 CHATO JULIE KENT 3 3 160 80
2017 175 231 2 CHATO JULIE KENT 3 4 82 90
2017 176 231 2 CHATO JULIE KENT 3 5 110 90
2017 177 231 2 CHATO JULIE KENT 3 6 140 80
2017 178 231 2 CHATO JULIE KENT 3 7 208 85
2017 179 231 2 CHATO JULIE KENT 3 8 240 90
2017 180 231 2 CHATO JULIE KENT 3 9 160 90
2017 181 111 6 CHULUCANAS IRWIN KENT 3 1 60 40
2017 182 111 6 CHULUCANAS IRWIN KENT 3 2 113 50
2017 183 111 6 CHULUCANAS IRWIN KENT 3 3 210 80
2017 184 111 6 CHULUCANAS IRWIN KENT 3 4 260 90
2017 185 111 6 CHULUCANAS IRWIN KENT 3 5 150 80
2017 186 111 6 CHULUCANAS IRWIN KENT 3 6 80 50
2017 187 111 6 CHULUCANAS IRWIN KENT 3 7 180 90
2017 188 111 6 CHULUCANAS IRWIN KENT 3 8 196 80
2017 189 111 6 CHULUCANAS IRWIN KENT 3 9 220 80
2017 190 221 7 CHATO CHATO KENT 3 1 230 90
2017 191 221 7 CHATO CHATO KENT 3 2 210 90
2017 192 221 7 CHATO CHATO KENT 3 3 150 85
2017 193 221 7 CHATO CHATO KENT 3 4 315 90
2017 194 221 7 CHATO CHATO KENT 3 5 220 90
2017 195 221 7 CHATO CHATO KENT 3 6
2017 196 221 7 CHATO CHATO KENT 3 7 250 90
2017 197 221 7 CHATO CHATO KENT 3 8 160 80
2017 198 221 7 CHATO CHATO KENT 3 9 230 90
2017 199 131 5 CHULUCANAS JULIE KENT 3 1
2017 200 131 5 CHULUCANAS JULIE KENT 3 2 280 90
2017 201 131 5 CHULUCANAS JULIE KENT 3 3 103 90
2017 202 131 5 CHULUCANAS JULIE KENT 3 4 180 90
2017 203 131 5 CHULUCANAS JULIE KENT 3 5 250 90
2017 204 131 5 CHULUCANAS JULIE KENT 3 6
2017 205 131 5 CHULUCANAS JULIE KENT 3 7 190 90
2017 206 131 5 CHULUCANAS JULIE KENT 3 8 140 90
2017 207 131 5 CHULUCANAS JULIE KENT 3 9 210 80
2017 208 241 3 CHULUCANAS CHATO KENT 3 1 190 80
2017 209 241 3 CHULUCANAS CHATO KENT 3 2 220 90
2017 210 241 3 CHULUCANAS CHATO KENT 3 3 140 80
2017 211 241 3 CHULUCANAS CHATO KENT 3 4 210 75
2017 212 241 3 CHULUCANAS CHATO KENT 3 5 200 90
2017 213 241 3 CHULUCANAS CHATO KENT 3 6 290 90
2017 214 241 3 CHULUCANAS CHATO KENT 3 7 140 50
2017 215 241 3 CHULUCANAS CHATO KENT 3 8 170 90
2017 216 241 3 CHULUCANAS CHATO KENT 3 9 160 85
2018 1 141 1 CHULUCANAS CHULUCANAS KENT 1 1 95
2018 2 141 1 CHULUCANAS CHULUCANAS KENT 1 2 95
2018 3 141 1 CHULUCANAS CHULUCANAS KENT 1 3 315
2018 4 141 1 CHULUCANAS CHULUCANAS KENT 1 4
2018 5 141 1 CHULUCANAS CHULUCANAS KENT 1 5 230
2018 6 141 1 CHULUCANAS CHULUCANAS KENT 1 6 232
2018 7 141 1 CHULUCANAS CHULUCANAS KENT 1 7 80
2018 8 141 1 CHULUCANAS CHULUCANAS KENT 1 8 90
2018 9 141 1 CHULUCANAS CHULUCANAS KENT 1 9 20
2018 10 231 2 CHATO JULIE KENT 1 1 80
2018 11 231 2 CHATO JULIE KENT 1 2 50
2018 12 231 2 CHATO JULIE KENT 1 3 92
2018 13 231 2 CHATO JULIE KENT 1 4 163
2018 14 231 2 CHATO JULIE KENT 1 5 5
2018 15 231 2 CHATO JULIE KENT 1 6 145
2018 16 231 2 CHATO JULIE KENT 1 7 80
2018 17 231 2 CHATO JULIE KENT 1 8 40
2018 18 231 2 CHATO JULIE KENT 1 9
2018 19 121 8 CHATO CHULUCANAS KENT 1 1
2018 20 121 8 CHATO CHULUCANAS KENT 1 2 90
2018 21 121 8 CHATO CHULUCANAS KENT 1 3 90
2018 22 121 8 CHATO CHULUCANAS KENT 1 4 295
2018 23 121 8 CHATO CHULUCANAS KENT 1 5 382
2018 24 121 8 CHATO CHULUCANAS KENT 1 6 270
2018 25 121 8 CHATO CHULUCANAS KENT 1 7 90
2018 26 121 8 CHATO CHULUCANAS KENT 1 8 90
2018 27 121 8 CHATO CHULUCANAS KENT 1 9 50
2018 28 211 4 CHATO IRWIN KENT 1 1 50
2018 29 211 4 CHATO IRWIN KENT 1 2 60
2018 30 211 4 CHATO IRWIN KENT 1 3 242
2018 31 211 4 CHATO IRWIN KENT 1 4 70
2018 32 211 4 CHATO IRWIN KENT 1 5 320
2018 33 211 4 CHATO IRWIN KENT 1 6 300
2018 34 211 4 CHATO IRWIN KENT 1 7 80
2018 35 211 4 CHATO IRWIN KENT 1 8 90
2018 36 211 4 CHATO IRWIN KENT 1 9 95
2018 37 131 5 CHULUCANAS JULIE KENT 1 1 25
2018 38 131 5 CHULUCANAS JULIE KENT 1 2 250
2018 39 131 5 CHULUCANAS JULIE KENT 1 3 80
2018 40 131 5 CHULUCANAS JULIE KENT 1 4 240
2018 41 131 5 CHULUCANAS JULIE KENT 1 5 200
2018 42 131 5 CHULUCANAS JULIE KENT 1 6 10
2018 43 131 5 CHULUCANAS JULIE KENT 1 7 75
2018 44 131 5 CHULUCANAS JULIE KENT 1 8 60
2018 45 131 5 CHULUCANAS JULIE KENT 1 9 90
2018 46 241 3 CHULUCANAS CHATO KENT 1 1 20
2018 47 241 3 CHULUCANAS CHATO KENT 1 2 40
2018 48 241 3 CHULUCANAS CHATO KENT 1 3 70
2018 49 241 3 CHULUCANAS CHATO KENT 1 4 250
2018 50 241 3 CHULUCANAS CHATO KENT 1 5 195
2018 51 241 3 CHULUCANAS CHATO KENT 1 6 280
2018 52 241 3 CHULUCANAS CHATO KENT 1 7 30
2018 53 241 3 CHULUCANAS CHATO KENT 1 8 80
2018 54 241 3 CHULUCANAS CHATO KENT 1 9 90
2018 55 111 6 CHULUCANAS IRWIN KENT 1 1 80
2018 56 111 6 CHULUCANAS IRWIN KENT 1 2 146
2018 57 111 6 CHULUCANAS IRWIN KENT 1 3 15
2018 58 111 6 CHULUCANAS IRWIN KENT 1 4 138
2018 59 111 6 CHULUCANAS IRWIN KENT 1 5 90
2018 60 111 6 CHULUCANAS IRWIN KENT 1 6 190
2018 61 111 6 CHULUCANAS IRWIN KENT 1 7
2018 62 111 6 CHULUCANAS IRWIN KENT 1 8 80
2018 63 111 6 CHULUCANAS IRWIN KENT 1 9 95
2018 64 221 7 CHATO CHATO KENT 1 1 180
2018 65 221 7 CHATO CHATO KENT 1 2 50
2018 66 221 7 CHATO CHATO KENT 1 3 80
2018 67 221 7 CHATO CHATO KENT 1 4 80
2018 68 221 7 CHATO CHATO KENT 1 5 90
2018 69 221 7 CHATO CHATO KENT 1 6 178
2018 70 221 7 CHATO CHATO KENT 1 7
2018 71 221 7 CHATO CHATO KENT 1 8 204
2018 72 221 7 CHATO CHATO KENT 1 9 97
2018 73 211 4 CHATO IRWIN KENT 2 1
2018 74 211 4 CHATO IRWIN KENT 2 2 266 90
2018 75 211 4 CHATO IRWIN KENT 2 3 187 85
2018 76 211 4 CHATO IRWIN KENT 2 4 5
2018 77 211 4 CHATO IRWIN KENT 2 5 220 90
2018 78 211 4 CHATO IRWIN KENT 2 6 35
2018 79 211 4 CHATO IRWIN KENT 2 7 90
2018 80 211 4 CHATO IRWIN KENT 2 8 95
2018 81 211 4 CHATO IRWIN KENT 2 9 90
2018 82 121 8 CHATO CHULUCANAS KENT 2 1 85
2018 83 121 8 CHATO CHULUCANAS KENT 2 2 108 90
2018 84 121 8 CHATO CHULUCANAS KENT 2 3 200 90
2018 85 121 8 CHATO CHULUCANAS KENT 2 4 95
2018 86 121 8 CHATO CHULUCANAS KENT 2 5 95
2018 87 121 8 CHATO CHULUCANAS KENT 2 6 90
2018 88 121 8 CHATO CHULUCANAS KENT 2 7 280 85
2018 89 121 8 CHATO CHULUCANAS KENT 2 8 90
2018 90 121 8 CHATO CHULUCANAS KENT 2 9 90
2018 91 231 2 CHATO JULIE KENT 2 1
2018 92 231 2 CHATO JULIE KENT 2 2 300 85
2018 93 231 2 CHATO JULIE KENT 2 3 290 80
2018 94 231 2 CHATO JULIE KENT 2 4 50
2018 95 231 2 CHATO JULIE KENT 2 5 214 90
2018 96 231 2 CHATO JULIE KENT 2 6 90
2018 97 231 2 CHATO JULIE KENT 2 7 30
2018 98 231 2 CHATO JULIE KENT 2 8 85
2018 99 231 2 CHATO JULIE KENT 2 9 85
2018 100 141 1 CHULUCANAS CHULUCANAS KENT 2 1 305 85
2018 101 141 1 CHULUCANAS CHULUCANAS KENT 2 2 85
2018 102 141 1 CHULUCANAS CHULUCANAS KENT 2 3 75
2018 103 141 1 CHULUCANAS CHULUCANAS KENT 2 4 80
2018 104 141 1 CHULUCANAS CHULUCANAS KENT 2 5 230 75
2018 105 141 1 CHULUCANAS CHULUCANAS KENT 2 6 150 95
2018 106 141 1 CHULUCANAS CHULUCANAS KENT 2 7 80
2018 107 141 1 CHULUCANAS CHULUCANAS KENT 2 8 95
2018 108 141 1 CHULUCANAS CHULUCANAS KENT 2 9 80
2018 109 221 7 CHATO CHATO KENT 2 1 60
2018 110 221 7 CHATO CHATO KENT 2 2 80
2018 111 221 7 CHATO CHATO KENT 2 3 5
2018 112 221 7 CHATO CHATO KENT 2 4 3
2018 113 221 7 CHATO CHATO KENT 2 5 150 70
2018 114 221 7 CHATO CHATO KENT 2 6 140 30
2018 115 221 7 CHATO CHATO KENT 2 7 0
2018 116 221 7 CHATO CHATO KENT 2 8 150 10
2018 117 221 7 CHATO CHATO KENT 2 9 80
2018 118 111 6 CHULUCANAS IRWIN KENT 2 1
2018 119 111 6 CHULUCANAS IRWIN KENT 2 2 95
2018 120 111 6 CHULUCANAS IRWIN KENT 2 3 90
2018 121 111 6 CHULUCANAS IRWIN KENT 2 4 85
2018 122 111 6 CHULUCANAS IRWIN KENT 2 5 10
2018 123 111 6 CHULUCANAS IRWIN KENT 2 6 5
2018 124 111 6 CHULUCANAS IRWIN KENT 2 7 160 70
2018 125 111 6 CHULUCANAS IRWIN KENT 2 8 325 90
2018 126 111 6 CHULUCANAS IRWIN KENT 2 9 265 95
2018 127 241 3 CHULUCANAS CHATO KENT 2 1 80
2018 128 241 3 CHULUCANAS CHATO KENT 2 2 20
2018 129 241 3 CHULUCANAS CHATO KENT 2 3 65 20
2018 130 241 3 CHULUCANAS CHATO KENT 2 4 61 20
2018 131 241 3 CHULUCANAS CHATO KENT 2 5
2018 132 241 3 CHULUCANAS CHATO KENT 2 6 255 95
2018 133 241 3 CHULUCANAS CHATO KENT 2 7 40
2018 134 241 3 CHULUCANAS CHATO KENT 2 8 60
2018 135 241 3 CHULUCANAS CHATO KENT 2 9 50
2018 136 131 5 CHULUCANAS JULIE KENT 2 1 20
2018 137 131 5 CHULUCANAS JULIE KENT 2 2 95
2018 138 131 5 CHULUCANAS JULIE KENT 2 3 124 90
2018 139 131 5 CHULUCANAS JULIE KENT 2 4 220 90
2018 140 131 5 CHULUCANAS JULIE KENT 2 5 260 95
2018 141 131 5 CHULUCANAS JULIE KENT 2 6
2018 142 131 5 CHULUCANAS JULIE KENT 2 7 90
2018 143 131 5 CHULUCANAS JULIE KENT 2 8 95
2018 144 131 5 CHULUCANAS JULIE KENT 2 9 95
2018 145 121 8 CHATO CHULUCANAS KENT 3 1 95
2018 146 121 8 CHATO CHULUCANAS KENT 3 2 95
2018 147 121 8 CHATO CHULUCANAS KENT 3 3 95
2018 148 121 8 CHATO CHULUCANAS KENT 3 4 110 90
2018 149 121 8 CHATO CHULUCANAS KENT 3 5 147 95
2018 150 121 8 CHATO CHULUCANAS KENT 3 6 100
2018 151 121 8 CHATO CHULUCANAS KENT 3 7 75
2018 152 121 8 CHATO CHULUCANAS KENT 3 8 390 90
2018 153 121 8 CHATO CHULUCANAS KENT 3 9 95
2018 154 211 4 CHATO IRWIN KENT 3 1 80
2018 155 211 4 CHATO IRWIN KENT 3 2 95
2018 156 211 4 CHATO IRWIN KENT 3 3 40
2018 157 211 4 CHATO IRWIN KENT 3 4 160 65
2018 158 211 4 CHATO IRWIN KENT 3 5
2018 159 211 4 CHATO IRWIN KENT 3 6 126 75
2018 160 211 4 CHATO IRWIN KENT 3 7 20
2018 161 211 4 CHATO IRWIN KENT 3 8 136 25
2018 162 211 4 CHATO IRWIN KENT 3 9 40
2018 163 141 1 CHULUCANAS CHULUCANAS KENT 3 1 390 80
2018 164 141 1 CHULUCANAS CHULUCANAS KENT 3 2 95
2018 165 141 1 CHULUCANAS CHULUCANAS KENT 3 3 90
2018 166 141 1 CHULUCANAS CHULUCANAS KENT 3 4 70
2018 167 141 1 CHULUCANAS CHULUCANAS KENT 3 5 250 95
2018 168 141 1 CHULUCANAS CHULUCANAS KENT 3 6 95
2018 169 141 1 CHULUCANAS CHULUCANAS KENT 3 7 0
2018 170 141 1 CHULUCANAS CHULUCANAS KENT 3 8 272 90
2018 171 141 1 CHULUCANAS CHULUCANAS KENT 3 9 95
2018 172 231 2 CHATO JULIE KENT 3 1 203 50
2018 173 231 2 CHATO JULIE KENT 3 2 0
2018 174 231 2 CHATO JULIE KENT 3 3 0
2018 175 231 2 CHATO JULIE KENT 3 4 5
2018 176 231 2 CHATO JULIE KENT 3 5 2
2018 177 231 2 CHATO JULIE KENT 3 6 33 15
2018 178 231 2 CHATO JULIE KENT 3 7 116 40
2018 179 231 2 CHATO JULIE KENT 3 8 60
2018 180 231 2 CHATO JULIE KENT 3 9 50
2018 181 111 6 CHULUCANAS IRWIN KENT 3 1 90
2018 182 111 6 CHULUCANAS IRWIN KENT 3 2 95
2018 183 111 6 CHULUCANAS IRWIN KENT 3 3 245 90
2018 184 111 6 CHULUCANAS IRWIN KENT 3 4 290 90
2018 185 111 6 CHULUCANAS IRWIN KENT 3 5 70
2018 186 111 6 CHULUCANAS IRWIN KENT 3 6 85
2018 187 111 6 CHULUCANAS IRWIN KENT 3 7 90
2018 188 111 6 CHULUCANAS IRWIN KENT 3 8 80
2018 189 111 6 CHULUCANAS IRWIN KENT 3 9 160 50
2018 190 221 7 CHATO CHATO KENT 3 1 220 80
2018 191 221 7 CHATO CHATO KENT 3 2 10
2018 192 221 7 CHATO CHATO KENT 3 3 0
2018 193 221 7 CHATO CHATO KENT 3 4 182 50
2018 194 221 7 CHATO CHATO KENT 3 5 0
2018 195 221 7 CHATO CHATO KENT 3 6
2018 196 221 7 CHATO CHATO KENT 3 7 180 80
2018 197 221 7 CHATO CHATO KENT 3 8 5
2018 198 221 7 CHATO CHATO KENT 3 9 2
2018 199 131 5 CHULUCANAS JULIE KENT 3 1
2018 200 131 5 CHULUCANAS JULIE KENT 3 2 85
2018 201 131 5 CHULUCANAS JULIE KENT 3 3 70
2018 202 131 5 CHULUCANAS JULIE KENT 3 4 213 70
2018 203 131 5 CHULUCANAS JULIE KENT 3 5 300 90
2018 204 131 5 CHULUCANAS JULIE KENT 3 6
2018 205 131 5 CHULUCANAS JULIE KENT 3 7 10
2018 206 131 5 CHULUCANAS JULIE KENT 3 8 20
2018 207 131 5 CHULUCANAS JULIE KENT 3 9 200 60
2018 208 241 3 CHULUCANAS CHATO KENT 3 1 80
2018 209 241 3 CHULUCANAS CHATO KENT 3 2 25
2018 210 241 3 CHULUCANAS CHATO KENT 3 3 60
2018 211 241 3 CHULUCANAS CHATO KENT 3 4 210 60
2018 212 241 3 CHULUCANAS CHATO KENT 3 5 75 15
2018 213 241 3 CHULUCANAS CHATO KENT 3 6 188 40
2018 214 241 3 CHULUCANAS CHATO KENT 3 7 10
2018 215 241 3 CHULUCANAS CHATO KENT 3 8 80
2018 216 241 3 CHULUCANAS CHATO KENT 3 9 40
2019 1 141 1 CHULUCANAS CHULUCANAS KENT 1 1 3.15 180 95 80
2019 2 141 1 CHULUCANAS CHULUCANAS KENT 1 2 3.60 280 90 75
2019 3 141 1 CHULUCANAS CHULUCANAS KENT 1 3 3.65 234 80 70
2019 4 141 1 CHULUCANAS CHULUCANAS KENT 1 4
2019 5 141 1 CHULUCANAS CHULUCANAS KENT 1 5 3.00 110 90 40
2019 6 141 1 CHULUCANAS CHULUCANAS KENT 1 6 3.40 202 95 80
2019 7 141 1 CHULUCANAS CHULUCANAS KENT 1 7 3.40 202 80 75
2019 8 141 1 CHULUCANAS CHULUCANAS KENT 1 8 3.20 108 70 50
2019 9 141 1 CHULUCANAS CHULUCANAS KENT 1 9 2.70 80 90 0
2019 10 231 2 CHATO JULIE KENT 1 1 3.65 245 85 5
2019 11 231 2 CHATO JULIE KENT 1 2 3.95 228 80 25
2019 12 231 2 CHATO JULIE KENT 1 3 4.85 320 95 20
2019 13 231 2 CHATO JULIE KENT 1 4 4.35 307 90 40
2019 14 231 2 CHATO JULIE KENT 1 5 4.00 251 80 75
2019 15 231 2 CHATO JULIE KENT 1 6 4.20 264 70 35
2019 16 231 2 CHATO JULIE KENT 1 7 4.25 280 95 65
2019 17 231 2 CHATO JULIE KENT 1 8 4.80 288 95 80
2019 18 231 2 CHATO JULIE KENT 1 9 70
2019 19 121 8 CHATO CHULUCANAS KENT 1 1 4.05 70
2019 20 121 8 CHATO CHULUCANAS KENT 1 2 4.00 122 90 80
2019 21 121 8 CHATO CHULUCANAS KENT 1 3 3.70 118 90 80
2019 22 121 8 CHATO CHULUCANAS KENT 1 4 4.25 152 90 75
2019 23 121 8 CHATO CHULUCANAS KENT 1 5 4.35 333 90 40
2019 24 121 8 CHATO CHULUCANAS KENT 1 6 3.70 221 95 75
2019 25 121 8 CHATO CHULUCANAS KENT 1 7 4.50 304 85 90
2019 26 121 8 CHATO CHULUCANAS KENT 1 8 4.15 182 80 90
2019 27 121 8 CHATO CHULUCANAS KENT 1 9 3.95 210 95 70
2019 28 211 4 CHATO IRWIN KENT 1 1 4.20 395 90 30
2019 29 211 4 CHATO IRWIN KENT 1 2 3.95 160 90 75
2019 30 211 4 CHATO IRWIN KENT 1 3 4.15 226 90 70
2019 31 211 4 CHATO IRWIN KENT 1 4 4.45 194 85 70
2019 32 211 4 CHATO IRWIN KENT 1 5 4.10 315 75 60
2019 33 211 4 CHATO IRWIN KENT 1 6 4.45 130 65 30
2019 34 211 4 CHATO IRWIN KENT 1 7 4.40 175 80 30
2019 35 211 4 CHATO IRWIN KENT 1 8 4.00 140 80 60
2019 36 211 4 CHATO IRWIN KENT 1 9 3.80 97 65 60
2019 37 131 5 CHULUCANAS JULIE KENT 1 1 3.00 125 90 80
2019 38 131 5 CHULUCANAS JULIE KENT 1 2 3.95 185 70 65
2019 39 131 5 CHULUCANAS JULIE KENT 1 3 4.10 203 80 90
2019 40 131 5 CHULUCANAS JULIE KENT 1 4 3.65 160 90 45
2019 41 131 5 CHULUCANAS JULIE KENT 1 5 3.65 137 90 60
2019 42 131 5 CHULUCANAS JULIE KENT 1 6 3.10 138 95 65
2019 43 131 5 CHULUCANAS JULIE KENT 1 7 3.70 184 95 80
2019 44 131 5 CHULUCANAS JULIE KENT 1 8 3.60 144 90 80
2019 45 131 5 CHULUCANAS JULIE KENT 1 9 3.80 210 85 75
2019 46 241 3 CHULUCANAS CHATO KENT 1 1 4.45 219 90 80
2019 47 241 3 CHULUCANAS CHATO KENT 1 2 4.65 240 90 70
2019 48 241 3 CHULUCANAS CHATO KENT 1 3 4.25 265 95 85
2019 49 241 3 CHULUCANAS CHATO KENT 1 4 4.35 215 95 75
2019 50 241 3 CHULUCANAS CHATO KENT 1 5 4.45 361 90 75
2019 51 241 3 CHULUCANAS CHATO KENT 1 6 4.05 144 80 75
2019 52 241 3 CHULUCANAS CHATO KENT 1 7 4.35 215 85 70
2019 53 241 3 CHULUCANAS CHATO KENT 1 8 4.10 104 90 45
2019 54 241 3 CHULUCANAS CHATO KENT 1 9 4.05 97 70 75
2019 55 111 6 CHULUCANAS IRWIN KENT 1 1 4.30 76 60 40
2019 56 111 6 CHULUCANAS IRWIN KENT 1 2 3.65 220 90 65
2019 57 111 6 CHULUCANAS IRWIN KENT 1 3 4.10 245 80 60
2019 58 111 6 CHULUCANAS IRWIN KENT 1 4 3.70 220 95 70
2019 59 111 6 CHULUCANAS IRWIN KENT 1 5 3.40 98 80 30
2019 60 111 6 CHULUCANAS IRWIN KENT 1 6 3.85 172 95 80
2019 61 111 6 CHULUCANAS IRWIN KENT 1 7
2019 62 111 6 CHULUCANAS IRWIN KENT 1 8 3.45 148 80 80
2019 63 111 6 CHULUCANAS IRWIN KENT 1 9
2019 64 221 7 CHATO CHATO KENT 1 1 4.45 206 95 20
2019 65 221 7 CHATO CHATO KENT 1 2 3.65 260 95 50
2019 66 221 7 CHATO CHATO KENT 1 3 3.40 132 95 70
2019 67 221 7 CHATO CHATO KENT 1 4 3.15 25 70
2019 68 221 7 CHATO CHATO KENT 1 5 3.35 51 80 70
2019 69 221 7 CHATO CHATO KENT 1 6 4.25 210 95 70
2019 70 221 7 CHATO CHATO KENT 1 7 3.30 65 80 80
2019 71 221 7 CHATO CHATO KENT 1 8 3.85 217 80 70
2019 72 221 7 CHATO CHATO KENT 1 9 3.00 65 85 70
2019 73 211 4 CHATO IRWIN KENT 2 1 4.85 23 30 20
2019 74 211 4 CHATO IRWIN KENT 2 2 4.05 164 90 70
2019 75 211 4 CHATO IRWIN KENT 2 3 3.95 187 70 70
2019 76 211 4 CHATO IRWIN KENT 2 4 3.10 34 70 40
2019 77 211 4 CHATO IRWIN KENT 2 5 3.05 167 80 65
2019 78 211 4 CHATO IRWIN KENT 2 6 3.40 130 90 45
2019 79 211 4 CHATO IRWIN KENT 2 7 4.05 312 60 60
2019 80 211 4 CHATO IRWIN KENT 2 8 3.75 136 60 40
2019 81 211 4 CHATO IRWIN KENT 2 9 3.70 139 85 65
2019 82 121 8 CHATO CHULUCANAS KENT 2 1 3.45 185 85 70
2019 83 121 8 CHATO CHULUCANAS KENT 2 2 3.75 58 90 50
2019 84 121 8 CHATO CHULUCANAS KENT 2 3 3.70 118 90 65
2019 85 121 8 CHATO CHULUCANAS KENT 2 4 3.75 152 70 60
2019 86 121 8 CHATO CHULUCANAS KENT 2 5 3.65 75 90
2019 87 121 8 CHATO CHULUCANAS KENT 2 6 4.05 187 90 70
2019 88 121 8 CHATO CHULUCANAS KENT 2 7 3.95 120 80 80
2019 89 121 8 CHATO CHULUCANAS KENT 2 8 3.40 150 90 40
2019 90 121 8 CHATO CHULUCANAS KENT 2 9 2.90 87 70 70
2019 91 231 2 CHATO JULIE KENT 2 1 2.00
2019 92 231 2 CHATO JULIE KENT 2 2 3.90 142 95 70
2019 93 231 2 CHATO JULIE KENT 2 3 3.90 260 90 75
2019 94 231 2 CHATO JULIE KENT 2 4 3.10 124 90 20
2019 95 231 2 CHATO JULIE KENT 2 5 3.60 132 80 70
2019 96 231 2 CHATO JULIE KENT 2 6 3.85 103 60 65
2019 97 231 2 CHATO JULIE KENT 2 7 3.65 104 85 65
2019 98 231 2 CHATO JULIE KENT 2 8 3.70 123 60 80
2019 99 231 2 CHATO JULIE KENT 2 9 3.35 139 70 90
2019 100 141 1 CHULUCANAS CHULUCANAS KENT 2 1 3.85 260 90 75
2019 101 141 1 CHULUCANAS CHULUCANAS KENT 2 2 3.55 215 90 35
2019 102 141 1 CHULUCANAS CHULUCANAS KENT 2 3 3.80 103 90 75
2019 103 141 1 CHULUCANAS CHULUCANAS KENT 2 4 3.45 181 90 65
2019 104 141 1 CHULUCANAS CHULUCANAS KENT 2 5 4.15 360 80 80
2019 105 141 1 CHULUCANAS CHULUCANAS KENT 2 6 3.45 98 60 20
2019 106 141 1 CHULUCANAS CHULUCANAS KENT 2 7 3.40 229 95 70
2019 107 141 1 CHULUCANAS CHULUCANAS KENT 2 8 3.30 159 90 70
2019 108 141 1 CHULUCANAS CHULUCANAS KENT 2 9 3.40 211 90 60
2019 109 221 7 CHATO CHATO KENT 2 1 3.35 88 70 40
2019 110 221 7 CHATO CHATO KENT 2 2 3.15 164 80 50
2019 111 221 7 CHATO CHATO KENT 2 3 3.30 127 70 50
2019 112 221 7 CHATO CHATO KENT 2 4 4.25 130 90 30
2019 113 221 7 CHATO CHATO KENT 2 5 2.80 218 70 65
2019 114 221 7 CHATO CHATO KENT 2 6 4.35 280 90 50
2019 115 221 7 CHATO CHATO KENT 2 7 3.95 125 80 50
2019 116 221 7 CHATO CHATO KENT 2 8 4.35 150 80 40
2019 117 221 7 CHATO CHATO KENT 2 9 3.50 102 80 50
2019 118 111 6 CHULUCANAS IRWIN KENT 2 1
2019 119 111 6 CHULUCANAS IRWIN KENT 2 2 3.30
2019 120 111 6 CHULUCANAS IRWIN KENT 2 3 3.20 95 90 60
2019 121 111 6 CHULUCANAS IRWIN KENT 2 4 3.60 134 70 60
2019 122 111 6 CHULUCANAS IRWIN KENT 2 5 3.70 205 70 30
2019 123 111 6 CHULUCANAS IRWIN KENT 2 6 4.10 152 90 30
2019 124 111 6 CHULUCANAS IRWIN KENT 2 7 3.90 125 90 60
2019 125 111 6 CHULUCANAS IRWIN KENT 2 8 4.20 219 70 80
2019 126 111 6 CHULUCANAS IRWIN KENT 2 9 3.50 241 80 80
2019 127 241 3 CHULUCANAS CHATO KENT 2 1 3.30 172 60
2019 128 241 3 CHULUCANAS CHATO KENT 2 2 3.50 220 85 75
2019 129 241 3 CHULUCANAS CHATO KENT 2 3 3.85 152 90 70
2019 130 241 3 CHULUCANAS CHATO KENT 2 4 3.95 130 90 50
2019 131 241 3 CHULUCANAS CHATO KENT 2 5 1.90 3 60 90
2019 132 241 3 CHULUCANAS CHATO KENT 2 6 1.85 216 80 70
2019 133 241 3 CHULUCANAS CHATO KENT 2 7 4.30 181 90 90
2019 134 241 3 CHULUCANAS CHATO KENT 2 8 3.45 176 80 70
2019 135 241 3 CHULUCANAS CHATO KENT 2 9 2.75 75 90 10
2019 136 131 5 CHULUCANAS JULIE KENT 2 1 2.75 120 90 70
2019 137 131 5 CHULUCANAS JULIE KENT 2 2 3.50 151 50 70
2019 138 131 5 CHULUCANAS JULIE KENT 2 3 3.40 135 80 80
2019 139 131 5 CHULUCANAS JULIE KENT 2 4 3.65 250 70 80
2019 140 131 5 CHULUCANAS JULIE KENT 2 5 3.10 188 70 75
2019 141 131 5 CHULUCANAS JULIE KENT 2 6
2019 142 131 5 CHULUCANAS JULIE KENT 2 7 2.90 121 90 40
2019 143 131 5 CHULUCANAS JULIE KENT 2 8 3.45 138 70 80
2019 144 131 5 CHULUCANAS JULIE KENT 2 9 2.90 145 90 70
2019 145 121 8 CHATO CHULUCANAS KENT 3 1 3.20 271 50 50
2019 146 121 8 CHATO CHULUCANAS KENT 3 2 3.40 221 40 60
2019 147 121 8 CHATO CHULUCANAS KENT 3 3 3.60 208 80 70
2019 148 121 8 CHATO CHULUCANAS KENT 3 4 3.30 168 50 45
2019 149 121 8 CHATO CHULUCANAS KENT 3 5 3.00 42 7 15
2019 150 121 8 CHATO CHULUCANAS KENT 3 6 2.85 150 50 65
2019 151 121 8 CHATO CHULUCANAS KENT 3 7 3.65 214 70 75
2019 152 121 8 CHATO CHULUCANAS KENT 3 8 3.65 346 70 80
2019 153 121 8 CHATO CHULUCANAS KENT 3 9 3.00 190 50 60
2019 154 211 4 CHATO IRWIN KENT 3 1 4.20 400 90 80
2019 155 211 4 CHATO IRWIN KENT 3 2 3.30 183 60
2019 156 211 4 CHATO IRWIN KENT 3 3 4.30 263 90 10
2019 157 211 4 CHATO IRWIN KENT 3 4 3.30 250 55 70
2019 158 211 4 CHATO IRWIN KENT 3 5
2019 159 211 4 CHATO IRWIN KENT 3 6 3.65 262 90 65
2019 160 211 4 CHATO IRWIN KENT 3 7 3.65 382 90 30
2019 161 211 4 CHATO IRWIN KENT 3 8 4.00 475 90 30
2019 162 211 4 CHATO IRWIN KENT 3 9 4.05 393 90 60
2019 163 141 1 CHULUCANAS CHULUCANAS KENT 3 1 4.15 470 90 70
2019 164 141 1 CHULUCANAS CHULUCANAS KENT 3 2 4.20 287 80 80
2019 165 141 1 CHULUCANAS CHULUCANAS KENT 3 3 4.15 414 80 80
2019 166 141 1 CHULUCANAS CHULUCANAS KENT 3 4 3.16 497 75 80
2019 167 141 1 CHULUCANAS CHULUCANAS KENT 3 5 3.85 279 90 75
2019 168 141 1 CHULUCANAS CHULUCANAS KENT 3 6 3.40 179 50 75
2019 169 141 1 CHULUCANAS CHULUCANAS KENT 3 7 3.40 208 95 20
2019 170 141 1 CHULUCANAS CHULUCANAS KENT 3 8 3.20 256 90 70
2019 171 141 1 CHULUCANAS CHULUCANAS KENT 3 9 2.80 104 80 75
2019 172 231 2 CHATO JULIE KENT 3 1 4.45 539 90 30
2019 173 231 2 CHATO JULIE KENT 3 2 3.70 402 90 20
2019 174 231 2 CHATO JULIE KENT 3 3 3.85 310 90 20
2019 175 231 2 CHATO JULIE KENT 3 4 3.30 103 90 50
2019 176 231 2 CHATO JULIE KENT 3 5 3.40 50 60 50
2019 177 231 2 CHATO JULIE KENT 3 6 3.40 200 80 10
2019 178 231 2 CHATO JULIE KENT 3 7 3.95 324 90 75
2019 179 231 2 CHATO JULIE KENT 3 8 4.05 354 90 75
2019 180 231 2 CHATO JULIE KENT 3 9 3.25 135 70 70
2019 181 111 6 CHULUCANAS IRWIN KENT 3 1 2.80 130 80 70
2019 182 111 6 CHULUCANAS IRWIN KENT 3 2 3.00 140 80 40
2019 183 111 6 CHULUCANAS IRWIN KENT 3 3 3.65 284 80 80
2019 184 111 6 CHULUCANAS IRWIN KENT 3 4 3.65 261 90 80
2019 185 111 6 CHULUCANAS IRWIN KENT 3 5 3.75 220 95 20
2019 186 111 6 CHULUCANAS IRWIN KENT 3 6 3.15 175 70 80
2019 187 111 6 CHULUCANAS IRWIN KENT 3 7 3.60 310 95 70
2019 188 111 6 CHULUCANAS IRWIN KENT 3 8 3.60 440 90 70
2019 189 111 6 CHULUCANAS IRWIN KENT 3 9 4.20 415 90 70
2019 190 221 7 CHATO CHATO KENT 3 1 3.70 360 50 70
2019 191 221 7 CHATO CHATO KENT 3 2 4.70 230 90 15
2019 192 221 7 CHATO CHATO KENT 3 3 4.10 350 90 20
2019 193 221 7 CHATO CHATO KENT 3 4 4.00 423 90 70
2019 194 221 7 CHATO CHATO KENT 3 5 3.50 304 90 60
2019 195 221 7 CHATO CHATO KENT 3 6
2019 196 221 7 CHATO CHATO KENT 3 7 4.00 410 90 80
2019 197 221 7 CHATO CHATO KENT 3 8 3.90 380 90 80
2019 198 221 7 CHATO CHATO KENT 3 9 4.05 340 90 50
2019 199 131 5 CHULUCANAS JULIE KENT 3 1
2019 200 131 5 CHULUCANAS JULIE KENT 3 2 3.70 268 80 80
2019 201 131 5 CHULUCANAS JULIE KENT 3 3 3.40 190 85 80
2019 202 131 5 CHULUCANAS JULIE KENT 3 4 3.75 214 80 80
2019 203 131 5 CHULUCANAS JULIE KENT 3 5 3.70 380 90 80
2019 204 131 5 CHULUCANAS JULIE KENT 3 6 214
2019 205 131 5 CHULUCANAS JULIE KENT 3 7 3.60 300 95 80
2019 206 131 5 CHULUCANAS JULIE KENT 3 8 3.40 325 90 60
2019 207 131 5 CHULUCANAS JULIE KENT 3 9 3.70 350 90 70
2019 208 241 3 CHULUCANAS CHATO KENT 3 1 3.50 180 80 75
2019 209 241 3 CHULUCANAS CHATO KENT 3 2 4.40 295 90 25
2019 210 241 3 CHULUCANAS CHATO KENT 3 3 3.60 232 70 50
2019 211 241 3 CHULUCANAS CHATO KENT 3 4 4.05 250 70 80
2019 212 241 3 CHULUCANAS CHATO KENT 3 5 4.10 340 90 80
2019 213 241 3 CHULUCANAS CHATO KENT 3 6 4.05 382 80 80
2019 214 241 3 CHULUCANAS CHATO KENT 3 7 3.70 379 90 70
2019 215 241 3 CHULUCANAS CHATO KENT 3 8 3.50 120 80 70
2019 216 241 3 CHULUCANAS CHATO KENT 3 9 3.65 260 90 70 

fru %>% kable(caption = "Evaluation of mango fruit quality", align = 'c')
Evaluation of mango fruit quality
year n treat n_treat stock edge scion block n_plant sample n_fruits weigth long diameter_1 diameter_2 diameter_average
2023 1 141 1 CHULUCANAS CHULUCANAS KENT 1 1 1 26 270 86 78 74 76.0
2023 2 141 1 CHULUCANAS CHULUCANAS KENT 1 1 2 26 520 111 90 83 86.5
2023 3 141 1 CHULUCANAS CHULUCANAS KENT 1 1 3 26 385 103 86 75 80.5
2023 4 141 1 CHULUCANAS CHULUCANAS KENT 1 1 4 26 230 86 70 69 69.5
2023 5 141 1 CHULUCANAS CHULUCANAS KENT 1 1 5 26 285 88 78 71 74.5
2023 6 141 1 CHULUCANAS CHULUCANAS KENT 1 2 1 35 600 116 96 87 91.5
2023 7 141 1 CHULUCANAS CHULUCANAS KENT 1 2 2 35 457 101 88 88 88.0
2023 8 141 1 CHULUCANAS CHULUCANAS KENT 1 2 3 35 520 115 88 81 84.5
2023 9 141 1 CHULUCANAS CHULUCANAS KENT 1 2 4 35 665 126 103 95 99.0
2023 10 141 1 CHULUCANAS CHULUCANAS KENT 1 2 5 35 305 91 78 80 79.0
2023 11 231 2 CHATO JULIE KENT 1 1 1 118 422 100 91 83 87.0
2023 12 231 2 CHATO JULIE KENT 1 1 2 118 377 102 84 76 80.0
2023 13 231 2 CHATO JULIE KENT 1 1 3 118 300 91 84 72 78.0
2023 14 231 2 CHATO JULIE KENT 1 1 4 118 455 106 88 82 85.0
2023 15 231 2 CHATO JULIE KENT 1 1 5 118 485 108 91 83 87.0
2023 16 231 2 CHATO JULIE KENT 1 2 1 102 420 104 87 83 85.0
2023 17 231 2 CHATO JULIE KENT 1 2 2 102 395 99 86 80 83.0
2023 18 231 2 CHATO JULIE KENT 1 2 3 102 335 98 87 70 78.5
2023 19 231 2 CHATO JULIE KENT 1 2 4 102 610 118 99 92 95.5
2023 20 231 2 CHATO JULIE KENT 1 2 5 102 530 109 98 90 94.0
2023 21 241 3 CHULUCANAS CHATO KENT 1 1 1 86 485 105 90 83 86.5
2023 22 241 3 CHULUCANAS CHATO KENT 1 1 2 86 460 103 89 80 84.5
2023 23 241 3 CHULUCANAS CHATO KENT 1 1 3 86 576 111 101 86 93.5
2023 24 241 3 CHULUCANAS CHATO KENT 1 1 4 86 610 114 97 91 94.0
2023 25 241 3 CHULUCANAS CHATO KENT 1 1 5 86 505 111 92 85 88.5
2023 26 241 3 CHULUCANAS CHATO KENT 1 2 1 105 475 109 88 84 86.0
2023 27 241 3 CHULUCANAS CHATO KENT 1 2 2 105 350 95 83 77 80.0
2023 28 241 3 CHULUCANAS CHATO KENT 1 2 3 105 345 96 86 74 80.0
2023 29 241 3 CHULUCANAS CHATO KENT 1 2 4 105 475 101 95 86 90.5
2023 30 241 3 CHULUCANAS CHATO KENT 1 2 5 105 490 111 97 83 90.0
2023 31 211 4 CHATO IRWIN KENT 1 1 1 16 560 104 99 90 94.5
2023 32 211 4 CHATO IRWIN KENT 1 1 2 16 381 96 83 82 82.5
2023 33 211 4 CHATO IRWIN KENT 1 1 3 16 556 108 95 89 92.0
2023 34 211 4 CHATO IRWIN KENT 1 1 4 16 432 100 89 82 85.5
2023 35 211 4 CHATO IRWIN KENT 1 1 5 16 475 103 89 81 85.0
2023 36 211 4 CHATO IRWIN KENT 1 2 1 97 600 112 99 89 94.0
2023 37 211 4 CHATO IRWIN KENT 1 2 2 97 485 108 93 85 89.0
2023 38 211 4 CHATO IRWIN KENT 1 2 3 97 500 108 91 88 89.5
2023 39 211 4 CHATO IRWIN KENT 1 2 4 97 550 111 96 86 91.0
2023 40 211 4 CHATO IRWIN KENT 1 2 5 97 695 119 106 94 100.0
2023 41 131 5 CHULUCANAS JULIE KENT 1 1 1 94 367 100 82 80 81.0
2023 42 131 5 CHULUCANAS JULIE KENT 1 1 2 94 448 110 89 78 83.5
2023 43 131 5 CHULUCANAS JULIE KENT 1 1 3 94 400 104 87 77 82.0
2023 44 131 5 CHULUCANAS JULIE KENT 1 1 4 94 409 106 86 78 82.0
2023 45 131 5 CHULUCANAS JULIE KENT 1 1 5 94 402 102 90 80 85.0
2023 46 131 5 CHULUCANAS JULIE KENT 1 2 1 118 460 107 86 80 83.0
2023 47 131 5 CHULUCANAS JULIE KENT 1 2 2 118 450 107 94 81 87.5
2023 48 131 5 CHULUCANAS JULIE KENT 1 2 3 118 416 108 82 77 79.5
2023 49 131 5 CHULUCANAS JULIE KENT 1 2 4 118 500 107 97 85 91.0
2023 50 131 5 CHULUCANAS JULIE KENT 1 2 5 118 465 112 93 82 87.5
2023 51 111 6 CHULUCANAS IRWIN KENT 1 1 1 91 605 120 94 91 92.5
2023 52 111 6 CHULUCANAS IRWIN KENT 1 1 2 91 585 122 93 86 89.5
2023 53 111 6 CHULUCANAS IRWIN KENT 1 1 3 91 415 106 85 79 82.0
2023 54 111 6 CHULUCANAS IRWIN KENT 1 1 4 91 460 103 89 81 85.0
2023 55 111 6 CHULUCANAS IRWIN KENT 1 1 5 91 455 105 86 82 84.0
2023 56 111 6 CHULUCANAS IRWIN KENT 1 2 1 71 375 96 88 79 83.5
2023 57 111 6 CHULUCANAS IRWIN KENT 1 2 2 71 455 108 94 84 89.0
2023 58 111 6 CHULUCANAS IRWIN KENT 1 2 3 71 410 101 84 77 80.5
2023 59 111 6 CHULUCANAS IRWIN KENT 1 2 4 71 460 110 89 79 84.0
2023 60 111 6 CHULUCANAS IRWIN KENT 1 2 5 71 385 105 82 78 80.0
2023 61 221 7 CHATO CHATO KENT 1 1 1 13 440 100 90 81 85.5
2023 62 221 7 CHATO CHATO KENT 1 1 2 13 450 104 90 84 87.0
2023 63 221 7 CHATO CHATO KENT 1 1 3 13 540 110 91 88 89.5
2023 64 221 7 CHATO CHATO KENT 1 1 4 13 368 97 82 81 81.5
2023 65 221 7 CHATO CHATO KENT 1 1 5 13 341 93 85 78 81.5
2023 66 221 7 CHATO CHATO KENT 1 2 1 31 500 117 92 79 85.5
2023 67 221 7 CHATO CHATO KENT 1 2 2 31 305 96 81 71 76.0
2023 68 221 7 CHATO CHATO KENT 1 2 3 31 425 103 89 82 85.5
2023 69 221 7 CHATO CHATO KENT 1 2 4 31 360 101 82 78 80.0
2023 70 221 7 CHATO CHATO KENT 1 2 5 31 380 101 84 78 81.0
2023 71 121 8 CHATO CHULUCANAS KENT 1 1 1 29 340 95 80 77 78.5
2023 72 121 8 CHATO CHULUCANAS KENT 1 1 2 29 390 100 82 77 79.5
2023 73 121 8 CHATO CHULUCANAS KENT 1 1 3 29 480 110 92 81 86.5
2023 74 121 8 CHATO CHULUCANAS KENT 1 1 4 29 440 102 87 80 83.5
2023 75 121 8 CHATO CHULUCANAS KENT 1 1 5 29 450 104 90 83 86.5
2023 76 121 8 CHATO CHULUCANAS KENT 1 2 1 62 371 100 83 78 80.5
2023 77 121 8 CHATO CHULUCANAS KENT 1 2 2 62 381 95 89 81 85.0
2023 78 121 8 CHATO CHULUCANAS KENT 1 2 3 62 380 102 87 79 83.0
2023 79 121 8 CHATO CHULUCANAS KENT 1 2 4 62 452 105 91 84 87.5
2023 80 121 8 CHATO CHULUCANAS KENT 1 2 5 62 455 104 90 84 87.0
2023 81 141 1 CHULUCANAS CHULUCANAS KENT 2 1 1 51 385 98 90 79 84.5
2023 82 141 1 CHULUCANAS CHULUCANAS KENT 2 1 2 51 610 119 96 87 91.5
2023 83 141 1 CHULUCANAS CHULUCANAS KENT 2 1 3 51 640 120 100 86 93.0
2023 84 141 1 CHULUCANAS CHULUCANAS KENT 2 1 4 51 430 103 92 80 86.0
2023 85 141 1 CHULUCANAS CHULUCANAS KENT 2 1 5 51 500 107 95 87 91.0
2023 86 141 1 CHULUCANAS CHULUCANAS KENT 2 2 1 38 630 118 101 91 96.0
2023 87 141 1 CHULUCANAS CHULUCANAS KENT 2 2 2 38 512 107 97 86 91.5
2023 88 141 1 CHULUCANAS CHULUCANAS KENT 2 2 3 38 652 126 102 83 92.5
2023 89 141 1 CHULUCANAS CHULUCANAS KENT 2 2 4 38 510 108 93 88 90.5
2023 90 141 1 CHULUCANAS CHULUCANAS KENT 2 2 5 38 495 110 95 84 89.5
2023 91 231 2 CHATO JULIE KENT 2 1 1 57 441 102 93 82 87.5
2023 92 231 2 CHATO JULIE KENT 2 1 2 57 471 105 88 83 85.5
2023 93 231 2 CHATO JULIE KENT 2 1 3 57 645 120 100 95 97.5
2023 94 231 2 CHATO JULIE KENT 2 1 4 57 525 114 90 85 87.5
2023 95 231 2 CHATO JULIE KENT 2 1 5 57 360 101 89 72 80.5
2023 96 231 2 CHATO JULIE KENT 2 2 1 116 555 115 99 87 93.0
2023 97 231 2 CHATO JULIE KENT 2 2 2 116 495 116 91 80 85.5
2023 98 231 2 CHATO JULIE KENT 2 2 3 116 512 114 93 84 88.5
2023 99 231 2 CHATO JULIE KENT 2 2 4 116 605 119 95 85 90.0
2023 100 231 2 CHATO JULIE KENT 2 2 5 116 520 109 99 87 93.0
2023 101 241 3 CHULUCANAS CHATO KENT 2 1 1 91 425 102 91 82 86.5
2023 102 241 3 CHULUCANAS CHATO KENT 2 1 2 91 600 118 101 88 94.5
2023 103 241 3 CHULUCANAS CHATO KENT 2 1 3 91 765 119 107 94 100.5
2023 104 241 3 CHULUCANAS CHATO KENT 2 1 4 91 426 108 87 76 81.5
2023 105 241 3 CHULUCANAS CHATO KENT 2 1 5 91 475 106 97 84 90.5
2023 106 241 3 CHULUCANAS CHATO KENT 2 2 1 94 545 109 95 86 90.5
2023 107 241 3 CHULUCANAS CHATO KENT 2 2 2 94 371 100 85 78 81.5
2023 108 241 3 CHULUCANAS CHATO KENT 2 2 3 94 470 102 89 85 87.0
2023 109 241 3 CHULUCANAS CHATO KENT 2 2 4 94 445 105 94 83 88.5
2023 110 241 3 CHULUCANAS CHATO KENT 2 2 5 94 575 115 96 85 90.5
2023 111 211 4 CHATO IRWIN KENT 2 1 1 123 489 110 95 83 89.0
2023 112 211 4 CHATO IRWIN KENT 2 1 2 123 370 99 88 78 83.0
2023 113 211 4 CHATO IRWIN KENT 2 1 3 123 472 108 93 82 87.5
2023 114 211 4 CHATO IRWIN KENT 2 1 4 123 460 108 91 78 84.5
2023 115 211 4 CHATO IRWIN KENT 2 1 5 123 391 103 89 77 83.0
2023 116 211 4 CHATO IRWIN KENT 2 2 1 89 450 106 88 80 84.0
2023 117 211 4 CHATO IRWIN KENT 2 2 2 89 535 107 98 89 93.5
2023 118 211 4 CHATO IRWIN KENT 2 2 3 89 410 104 87 75 81.0
2023 119 211 4 CHATO IRWIN KENT 2 2 4 89 490 106 95 83 89.0
2023 120 211 4 CHATO IRWIN KENT 2 2 5 89 376 98 88 74 81.0
2023 121 131 5 CHULUCANAS JULIE KENT 2 1 1 109 385 98 89 79 84.0
2023 122 131 5 CHULUCANAS JULIE KENT 2 1 2 109 400 100 85 80 82.5
2023 123 131 5 CHULUCANAS JULIE KENT 2 1 3 109 635 120 100 88 94.0
2023 124 131 5 CHULUCANAS JULIE KENT 2 1 4 109 616 123 93 81 87.0
2023 125 131 5 CHULUCANAS JULIE KENT 2 1 5 109 445 108 89 78 83.5
2023 126 131 5 CHULUCANAS JULIE KENT 2 2 1 63 585 120 96 81 88.5
2023 127 131 5 CHULUCANAS JULIE KENT 2 2 2 63 388 99 88 81 84.5
2023 128 131 5 CHULUCANAS JULIE KENT 2 2 3 63 717 129 106 87 96.5
2023 129 131 5 CHULUCANAS JULIE KENT 2 2 4 63 478 107 95 83 89.0
2023 130 131 5 CHULUCANAS JULIE KENT 2 2 5 63 445 108 89 78 83.5
2023 131 111 6 CHULUCANAS IRWIN KENT 2 1 1 126 460 101 94 83 88.5
2023 132 111 6 CHULUCANAS IRWIN KENT 2 1 2 126 440 99 89 86 87.5
2023 133 111 6 CHULUCANAS IRWIN KENT 2 1 3 126 535 112 96 86 91.0
2023 134 111 6 CHULUCANAS IRWIN KENT 2 1 4 126 560 118 95 87 91.0
2023 135 111 6 CHULUCANAS IRWIN KENT 2 1 5 126 420 106 84 78 81.0
2023 136 111 6 CHULUCANAS IRWIN KENT 2 2 1 56 380 100 84 77 80.5
2023 137 111 6 CHULUCANAS IRWIN KENT 2 2 2 56 368 98 85 75 80.0
2023 138 111 6 CHULUCANAS IRWIN KENT 2 2 3 56 500 114 92 84 88.0
2023 139 111 6 CHULUCANAS IRWIN KENT 2 2 4 56 310 92 80 75 77.5
2023 140 111 6 CHULUCANAS IRWIN KENT 2 2 5 56 400 100 84 79 81.5
2023 141 221 7 CHATO CHATO KENT 2 1 1 103 405 100 89 79 84.0
2023 142 221 7 CHATO CHATO KENT 2 1 2 103 460 107 90 83 86.5
2023 143 221 7 CHATO CHATO KENT 2 1 3 103 470 107 90 81 85.5
2023 144 221 7 CHATO CHATO KENT 2 1 4 103 410 105 88 74 81.0
2023 145 221 7 CHATO CHATO KENT 2 1 5 103 610 115 103 92 97.5
2023 146 221 7 CHATO CHATO KENT 2 2 1 124 470 111 92 79 85.5
2023 147 221 7 CHATO CHATO KENT 2 2 2 124 435 109 86 79 82.5
2023 148 221 7 CHATO CHATO KENT 2 2 3 124 445 107 85 80 82.5
2023 149 221 7 CHATO CHATO KENT 2 2 4 124 430 104 91 79 85.0
2023 150 221 7 CHATO CHATO KENT 2 2 5 124 639 115 99 90 94.5
2023 151 121 8 CHATO CHULUCANAS KENT 2 1 1 95 615 119 98 89 93.5
2023 152 121 8 CHATO CHULUCANAS KENT 2 1 2 95 430 103 92 82 87.0
2023 153 121 8 CHATO CHULUCANAS KENT 2 1 3 95 460 104 89 84 86.5
2023 154 121 8 CHATO CHULUCANAS KENT 2 1 4 95 391 94 92 82 87.0
2023 155 121 8 CHATO CHULUCANAS KENT 2 1 5 95 410 100 93 80 86.5
2023 156 121 8 CHATO CHULUCANAS KENT 2 2 1 79 489 109 87 83 85.0
2023 157 121 8 CHATO CHULUCANAS KENT 2 2 2 79 569 113 98 89 93.5
2023 158 121 8 CHATO CHULUCANAS KENT 2 2 3 79 566 116 95 84 89.5
2023 159 121 8 CHATO CHULUCANAS KENT 2 2 4 79 475 106 96 84 90.0
2023 160 121 8 CHATO CHULUCANAS KENT 2 2 5 79 490 110 91 82 86.5
2023 161 141 1 CHULUCANAS CHULUCANAS KENT 3 1 1 85 361 95 86 78 82.0
2023 162 141 1 CHULUCANAS CHULUCANAS KENT 3 1 2 85 460 105 93 81 87.0
2023 163 141 1 CHULUCANAS CHULUCANAS KENT 3 1 3 85 465 103 94 82 88.0
2023 164 141 1 CHULUCANAS CHULUCANAS KENT 3 1 4 85 450 108 84 80 82.0
2023 165 141 1 CHULUCANAS CHULUCANAS KENT 3 1 5 85 552 111 97 84 90.5
2023 166 141 1 CHULUCANAS CHULUCANAS KENT 3 2 1 113 351 96 84 77 80.5
2023 167 141 1 CHULUCANAS CHULUCANAS KENT 3 2 2 113 584 117 97 84 90.5
2023 168 141 1 CHULUCANAS CHULUCANAS KENT 3 2 3 113 440 109 89 77 83.0
2023 169 141 1 CHULUCANAS CHULUCANAS KENT 3 2 4 113 420 103 90 80 85.0
2023 170 141 1 CHULUCANAS CHULUCANAS KENT 3 2 5 113 361 100 86 72 79.0
2023 171 231 2 CHATO JULIE KENT 3 1 1 57 645 120 102 89 95.5
2023 172 231 2 CHATO JULIE KENT 3 1 2 57 440 105 83 79 81.0
2023 173 231 2 CHATO JULIE KENT 3 1 3 57 300 97 81 70 75.5
2023 174 231 2 CHATO JULIE KENT 3 1 4 57 495 113 96 81 88.5
2023 175 231 2 CHATO JULIE KENT 3 1 5 57 460 111 88 78 83.0
2023 176 231 2 CHATO JULIE KENT 3 2 1 53 360 102 89 72 80.5
2023 177 231 2 CHATO JULIE KENT 3 2 2 53 444 108 86 82 84.0
2023 178 231 2 CHATO JULIE KENT 3 2 3 53 489 112 91 83 87.0
2023 179 231 2 CHATO JULIE KENT 3 2 4 53 488 111 93 82 87.5
2023 180 231 2 CHATO JULIE KENT 3 2 5 53 400 101 87 79 83.0
2023 181 241 3 CHULUCANAS CHATO KENT 3 1 1 87 655 123 101 91 96.0
2023 182 241 3 CHULUCANAS CHATO KENT 3 1 2 87 392 103 88 78 83.0
2023 183 241 3 CHULUCANAS CHATO KENT 3 1 3 87 316 96 83 73 78.0
2023 184 241 3 CHULUCANAS CHATO KENT 3 1 4 87 420 101 86 83 84.5
2023 185 241 3 CHULUCANAS CHATO KENT 3 1 5 87 410 103 87 79 83.0
2023 186 241 3 CHULUCANAS CHATO KENT 3 2 1 64 490 108 88 82 85.0
2023 187 241 3 CHULUCANAS CHATO KENT 3 2 2 64 400 95 89 82 85.5
2023 188 241 3 CHULUCANAS CHATO KENT 3 2 3 64 575 112 96 89 92.5
2023 189 241 3 CHULUCANAS CHATO KENT 3 2 4 64 613 112 98 91 94.5
2023 190 241 3 CHULUCANAS CHATO KENT 3 2 5 64 410 103 88 80 84.0
2023 191 211 4 CHATO IRWIN KENT 3 1 1 92 530 113 99 88 93.5
2023 192 211 4 CHATO IRWIN KENT 3 1 2 92 391 101 90 77 83.5
2023 193 211 4 CHATO IRWIN KENT 3 1 3 92 418 102 89 77 83.0
2023 194 211 4 CHATO IRWIN KENT 3 1 4 92 450 100 87 83 85.0
2023 195 211 4 CHATO IRWIN KENT 3 1 5 92 550 112 97 85 91.0
2023 196 211 4 CHATO IRWIN KENT 3 2 1 96 510 112 93 80 86.5
2023 197 211 4 CHATO IRWIN KENT 3 2 2 96 700 124 102 90 96.0
2023 198 211 4 CHATO IRWIN KENT 3 2 3 96 470 104 89 87 88.0
2023 199 211 4 CHATO IRWIN KENT 3 2 4 96 350 95 82 77 79.5
2023 200 211 4 CHATO IRWIN KENT 3 2 5 96 415 101 89 80 84.5
2023 201 131 5 CHULUCANAS JULIE KENT 3 1 1 64 450 108 91 78 84.5
2023 202 131 5 CHULUCANAS JULIE KENT 3 1 2 64 420 109 90 75 82.5
2023 203 131 5 CHULUCANAS JULIE KENT 3 1 3 64 640 120 104 90 97.0
2023 204 131 5 CHULUCANAS JULIE KENT 3 1 4 64 520 111 91 83 87.0
2023 205 131 5 CHULUCANAS JULIE KENT 3 1 5 64 625 126 95 86 90.5
2023 206 131 5 CHULUCANAS JULIE KENT 3 2 1 162 490 110 89 82 85.5
2023 207 131 5 CHULUCANAS JULIE KENT 3 2 2 162 515 107 92 84 88.0
2023 208 131 5 CHULUCANAS JULIE KENT 3 2 3 162 390 103 85 75 80.0
2023 209 131 5 CHULUCANAS JULIE KENT 3 2 4 162 465 105 89 80 84.5
2023 210 131 5 CHULUCANAS JULIE KENT 3 2 5 162 615 118 100 89 94.5
2023 211 111 6 CHULUCANAS IRWIN KENT 3 1 1 128 465 105 90 80 85.0
2023 212 111 6 CHULUCANAS IRWIN KENT 3 1 2 128 535 116 93 85 89.0
2023 213 111 6 CHULUCANAS IRWIN KENT 3 1 3 128 375 100 85 76 80.5
2023 214 111 6 CHULUCANAS IRWIN KENT 3 1 4 128 410 103 86 77 81.5
2023 215 111 6 CHULUCANAS IRWIN KENT 3 1 5 128 435 109 87 81 84.0
2023 216 111 6 CHULUCANAS IRWIN KENT 3 2 1 93 452 108 88 81 84.5
2023 217 111 6 CHULUCANAS IRWIN KENT 3 2 2 93 540 117 92 84 88.0
2023 218 111 6 CHULUCANAS IRWIN KENT 3 2 3 93 426 105 85 79 82.0
2023 219 111 6 CHULUCANAS IRWIN KENT 3 2 4 93 366 106 84 75 79.5
2023 220 111 6 CHULUCANAS IRWIN KENT 3 2 5 93 435 108 93 78 85.5
2023 221 221 7 CHATO CHATO KENT 3 1 1 113 615 120 101 85 93.0
2023 222 221 7 CHATO CHATO KENT 3 1 2 113 505 113 90 80 85.0
2023 223 221 7 CHATO CHATO KENT 3 1 3 113 430 107 86 78 82.0
2023 224 221 7 CHATO CHATO KENT 3 1 4 113 475 106 92 83 87.5
2023 225 221 7 CHATO CHATO KENT 3 1 5 113 325 93 81 72 76.5
2023 226 221 7 CHATO CHATO KENT 3 2 1 99 470 106 89 82 85.5
2023 227 221 7 CHATO CHATO KENT 3 2 2 99 480 109 96 80 88.0
2023 228 221 7 CHATO CHATO KENT 3 2 3 99 525 109 97 85 91.0
2023 229 221 7 CHATO CHATO KENT 3 2 4 99 452 113 94 77 85.5
2023 230 221 7 CHATO CHATO KENT 3 2 5 99 416 101 89 82 85.5
2023 231 121 8 CHATO CHULUCANAS KENT 3 1 1 103 470 108 90 81 85.5
2023 232 121 8 CHATO CHULUCANAS KENT 3 1 2 103 410 105 88 76 82.0
2023 233 121 8 CHATO CHULUCANAS KENT 3 1 3 103 450 111 89 81 85.0
2023 234 121 8 CHATO CHULUCANAS KENT 3 1 4 103 620 119 100 86 93.0
2023 235 121 8 CHATO CHULUCANAS KENT 3 1 5 103
2023 236 121 8 CHATO CHULUCANAS KENT 3 2 1 108 471 111 90 80 85.0
2023 237 121 8 CHATO CHULUCANAS KENT 3 2 2 108 475 110 93 81 87.0
2023 238 121 8 CHATO CHULUCANAS KENT 3 2 3 108 525 114 91 81 86.0
2023 239 121 8 CHATO CHULUCANAS KENT 3 2 4 108 426 100 86 82 84.0
2023 240 121 8 CHATO CHULUCANAS KENT 3 2 5 108 527 113 88 85 86.5

3 Data summary

Summary of the number of data points recorded for each treatment and evaluated variable.

sm <- rdt %>% 
  group_by(year, treat) %>% 
  summarise(across(height:sproud, ~ sum(!is.na(.))))

sm
## # A tibble: 24 × 6
## # Groups:   year [3]
##    year  treat height n_fruits flowering sproud
##    <fct> <fct>  <int>    <int>     <int>  <int>
##  1 2017  111        0       26        26      0
##  2 2017  121        0       26        26      0
##  3 2017  131        0       25        25      0
##  4 2017  141        0       26        26      0
##  5 2017  211        0       26        26      0
##  6 2017  221        0       26        26      0
##  7 2017  231        0       26        26      0
##  8 2017  241        0       27        27      0
##  9 2018  111        0        9        22      0
## 10 2018  121        0        9        23      0
## # ℹ 14 more rows

sm <- fru %>% 
  group_by(year, treat) %>% 
  summarise(across(weigth:diameter_average, ~ sum(!is.na(.))))

sm
## # A tibble: 8 × 7
## # Groups:   year [1]
##   year  treat weigth  long diameter_1 diameter_2 diameter_average
##   <fct> <fct>  <int> <int>      <int>      <int>            <int>
## 1 2023  111       30    30         30         30               30
## 2 2023  121       29    29         29         29               29
## 3 2023  131       30    30         30         30               30
## 4 2023  141       30    30         30         30               30
## 5 2023  211       30    30         30         30               30
## 6 2023  221       30    30         30         30               30
## 7 2023  231       30    30         30         30               30
## 8 2023  241       30    30         30         30               30

4 Meteorological data

Climatic conditions of the study area located in the Tambogrande district, Piura region.

met <- range_read(ss = gs, sheet = "clima") %>% 
  mutate(date = as_date(Fecha))

scale <- 2

plot <- met %>% 
  ggplot(aes(x = date)) +
  geom_line(aes(y = TMax, color = "Tmax (°C)"), size= 0.8, linetype = "longdash") + 
  geom_line(aes(y = TMin, color = "Tmin (°C)"), size= 0.8, linetype = "dotted") +
  geom_bar(aes(y = PP/scale)
            , stat="identity", size=.1, fill="blue", color="black", alpha=.4) +
  geom_line(aes(y = HR/scale, color = "HR (%)"), size = 0.8, linetype = "twodash") +
  scale_color_manual("", values = c("skyblue", "red", "blue")) +
  scale_y_continuous(limits = c(0, 50)
                     , expand = c(0, 0)
                     , name = "Temperature (°C)"
                     , sec.axis = sec_axis(~ . * scale, name = "Precipitation (mm)")
                     ) +
  scale_x_date(date_breaks = "3 month", date_labels = "%b-%Y", name = NULL) +
  theme_minimal_grid() +
  theme(legend.position = "top") +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))

plot %>% 
  ggsave2(plot = ., "submission/Figure_2.jpg", units = "cm"
         , width = 25, height = 15)

plot %>% 
  ggsave2(plot = ., "submission/Figure_2.eps", units = "cm"
         , width = 25, height = 15)

knitr::include_graphics("submission/Figure_2.jpg")

5 Objetives

Evaluate the effect of the rootstock-interstock interaction on the agronomic traits and fruit biometrics of the mango crop in the San Lorenzo Valley.

5.1 Specific Objective 1

Determine the effect of the rootstock-interstock interaction on the agronomic characteristics of mango.

5.1.1 Plant height

trait <- "height"

lmm <- paste({{trait}}, "~ 1 + (1|block) + stock*edge") %>% as.formula()

lmd <- paste({{trait}}, "~ block + stock*edge") %>% as.formula()

rmout <- rdt %>% 
  remove_outliers(formula = lmm
                  , drop_na = T, plot_diag = T)

rmout$diagplot


rmout$outliers
##     index block      stock  edge height      resi   res_MAD   rawp.BHStud
## 87    523     2      CHATO JULIE   2.00 -1.643199 -3.984232 0.00006769863
## 126   563     2 CHULUCANAS CHATO   1.90 -1.731293 -4.197830 0.00002694848
## 127   564     2 CHULUCANAS CHATO   1.85 -1.781293 -4.319064 0.00001566924
##              adjp       bholm out_flag
## 87  0.00006769863 0.013810521  OUTLIER
## 126 0.00002694848 0.005524438  OUTLIER
## 127 0.00001566924 0.003227863  OUTLIER

model <- rmout$data$clean %>% 
  aov(formula = lmd, .)

anova(model)
## Analysis of Variance Table
## 
## Response: height
##             Df Sum Sq Mean Sq F value   Pr(>F)    
## block        2  3.140 1.57012  9.1832 0.000155 ***
## stock        1  1.273 1.27268  7.4435 0.006954 ** 
## edge         3  1.994 0.66450  3.8865 0.009977 ** 
## stock:edge   3  2.301 0.76689  4.4853 0.004549 ** 
## Residuals  193 32.999 0.17098                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

mc <- emmeans(model, ~ edge|stock) %>%
  cld(Letters = letters, reversed = F) %>%
  mutate(across(.group, trimws)) %>% 
  rename(group = ".group")

mc %>% kable()
edge stock emmean SE df lower.CL upper.CL group
2 CHULUCANAS CHATO 3.662963 0.0795773 193 3.506010 3.819916 a
1 CHATO CHATO 3.741984 0.0811085 193 3.582011 3.901957 a
4 JULIE CHATO 3.860337 0.0827154 193 3.697194 4.023479 a
3 IRWIN CHATO 3.915061 0.0811085 193 3.755088 4.075034 a
8 JULIE CHULUCANAS 3.467553 0.0844563 193 3.300977 3.634129 a
6 CHULUCANAS CHULUCANAS 3.497320 0.0811085 193 3.337347 3.657293 a
7 IRWIN CHULUCANAS 3.649114 0.0844563 193 3.482538 3.815690 ab
5 CHATO CHULUCANAS 3.925048 0.0827647 193 3.761808 4.088287 b 

p1a <- mc %>% 
  plot_smr(x = "stock"
           , y = "emmean"
           , group = "edge"
           , sig = "group"
           , error = "SE"
           , color = T
           , xlab = "Rootstock"
           , ylab = "Plant height (m)"
           , glab = "Interstock"
           , ylimits = c(0, 6, 2)
           , 
           )

p1a

5.1.2 Sproud

trait <- "sproud"

lmm <- paste({{trait}}, "~ 1 + (1|block) + stock*edge") %>% as.formula()

lmd <- paste({{trait}}, "~ block + stock*edge") %>% as.formula()

rmout <- rdt %>% 
  remove_outliers(formula = lmm
                  , drop_na = T, plot_diag = T)

rmout$diagplot


rmout$outliers
##     index block      stock       edge sproud      resi   res_MAD  rawp.BHStud
## 8     441     1 CHULUCANAS CHULUCANAS      0 -63.26923 -4.267451 0.0000197719
## 126   567     2 CHULUCANAS      CHATO     10 -58.26923 -3.930206 0.0000848732
##             adjp       bholm out_flag
## 8   0.0000197719 0.003974153  OUTLIER
## 126 0.0000848732 0.016974639  OUTLIER

model <- rmout$data$clean %>% 
  aov(formula = lmd, .)

anova(model)
## Analysis of Variance Table
## 
## Response: sproud
##             Df Sum Sq Mean Sq F value     Pr(>F)    
## block        2    362   180.9  0.5152    0.59820    
## stock        1   6579  6578.9 18.7416 0.00002425 ***
## edge         3   2054   684.7  1.9506    0.12292    
## stock:edge   3   2713   904.2  2.5758    0.05521 .  
## Residuals  189  66345   351.0                       
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

mc <- emmeans(model, ~ edge|stock) %>%
  cld(Letters = letters, reversed = T) %>%
  mutate(across(.group, trimws)) %>% 
  rename(group = ".group")

mc %>% kable()
edge stock emmean SE df lower.CL upper.CL group
4 CHULUCANAS CHATO 65.17927 3.675132 189 57.92972 72.42882 a
3 CHATO CHATO 54.81356 3.747943 189 47.42039 62.20674 a
1 IRWIN CHATO 52.08471 3.750192 189 44.68710 59.48232 a
2 JULIE CHATO 51.91004 3.675132 189 44.66049 59.15959 a
7 JULIE CHULUCANAS 72.15745 3.826839 189 64.60864 79.70625 a
6 CHATO CHULUCANAS 70.57288 3.750234 189 63.17519 77.97057 a
5 CHULUCANAS CHULUCANAS 65.94241 3.750188 189 58.54481 73.34001 a
8 IRWIN CHULUCANAS 61.21227 3.910119 189 53.49919 68.92535 a 

p1b <- mc %>% 
  plot_smr(x = "stock"
           , y = "emmean"
           , group = "edge"
           , sig = "group"
           , error = "SE"
           , color = T
           , xlab = "Rootstock"
           , ylab = "Sproud ('%')"
           , glab = "Interstock"
           , ylimits = c(0, 100, 20)
           )

p1b

5.1.3 Number of fruits

trait <- "n_fruits"

lmm <- paste({{trait}}, "~ 1 + (1|block) + year + stock*edge + (1 + year|treat)") %>% as.formula()

lmd <- paste({{trait}}, "~ block + year + stock*edge") %>% as.formula()

rmout <- rdt %>% 
  remove_outliers(formula = lmm
                  , drop_na = T, plot_diag = T)

rmout$diagplot


rmout$outliers
##     index block year stock  edge treat n_fruits     resi  res_MAD   rawp.BHStud
## 442   604     3 2019 CHATO JULIE   231      539 287.2486 4.008689 0.00006105685
##              adjp      bholm out_flag
## 442 0.00006105685 0.02955151  OUTLIER

model <- rmout$data$clean %>% 
  aov(formula = lmd, .)

anova(model)
## Analysis of Variance Table
## 
## Response: n_fruits
##             Df  Sum Sq Mean Sq F value          Pr(>F)    
## block        2  261207  130604 21.6839 0.0000000009805 ***
## year         2  126248   63124 10.4804 0.0000352211768 ***
## stock        1    9326    9326  1.5483          0.2140    
## edge         3   22131    7377  1.2248          0.3001    
## stock:edge   3   15548    5183  0.8605          0.4615    
## Residuals  471 2836870    6023                            
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

mc <- emmeans(model, ~ year|edge|stock) %>%
  cld(Letters = letters, reversed = T) %>%
  mutate(across(.group, trimws)) %>% 
  rename(group = ".group")

mc %>% kable()
year edge stock emmean SE df lower.CL upper.CL group
1 2018 CHATO CHATO 197.2613 13.04649 471 171.6248 222.8978 a
3 2019 CHATO CHATO 194.2890 10.75243 471 173.1603 215.4177 a
2 2017 CHATO CHATO 162.5132 10.74838 471 141.3925 183.6339 b
13 2018 CHATO CHULUCANAS 205.1341 12.95537 471 179.6767 230.5916 a
15 2019 CHATO CHULUCANAS 202.1618 10.59847 471 181.3357 222.9880 a
14 2017 CHATO CHULUCANAS 170.3860 10.59352 471 149.5696 191.2024 b
4 2018 CHULUCANAS CHATO 206.5226 13.04648 471 180.8861 232.1591 a
6 2019 CHULUCANAS CHATO 203.5503 10.75303 471 182.4205 224.6802 a
5 2017 CHULUCANAS CHATO 171.7745 10.74778 471 150.6550 192.8940 b
16 2018 CHULUCANAS CHULUCANAS 233.6210 13.04648 471 207.9845 259.2575 a
18 2019 CHULUCANAS CHULUCANAS 230.6487 10.75303 471 209.5188 251.7785 a
17 2017 CHULUCANAS CHULUCANAS 198.8729 10.74778 471 177.7533 219.9924 b
7 2018 IRWIN CHATO 213.0482 13.04649 471 187.4117 238.6847 a
9 2019 IRWIN CHATO 210.0759 10.75243 471 188.9472 231.2046 a
8 2017 IRWIN CHATO 178.3001 10.74838 471 157.1794 199.4208 b
19 2018 IRWIN CHULUCANAS 209.5332 13.19501 471 183.6048 235.4616 a
21 2019 IRWIN CHULUCANAS 206.5609 11.07069 471 184.8068 228.3150 a
20 2017 IRWIN CHULUCANAS 174.7851 10.92618 471 153.3150 196.2552 b
10 2018 JULIE CHATO 207.7604 13.14195 471 181.9362 233.5845 a
12 2019 JULIE CHATO 204.7881 10.95974 471 183.2520 226.3241 a
11 2017 JULIE CHATO 173.0122 10.86339 471 151.6655 194.3589 b
22 2018 JULIE CHULUCANAS 211.3414 13.14337 471 185.5145 237.1683 a
24 2019 JULIE CHULUCANAS 208.3691 10.91480 471 186.9214 229.8169 a
23 2017 JULIE CHULUCANAS 176.5933 10.91078 471 155.1535 198.0331 b 

p1c <- mc %>% 
  plot_smr(type = "bar"
           , x = "year"
           , y = "emmean"
           , group = "edge"
           , sig = "group"
           , error = "SE"
           , color = T
           , xlab = "Year"
           , ylab = "Fruits number"
           , glab = "Interstock"
           , ylimits = c(0, 320, 60)
           ) +
  facet_wrap(. ~ stock, nrow = 2)

p1c

5.1.4 Flowering

trait <- "flowering"

lmm <- paste({{trait}}, "~ 1 + (1|block) + year + stock*edge + (1 + year|treat)") %>% as.formula()

lmd <- paste({{trait}}, "~ block + year + stock*edge") %>% as.formula()

rmout <- rdt %>% 
  remove_outliers(formula = lmm
                  , drop_na = T, plot_diag = T)

rmout$diagplot


rmout$outliers
##     index block year      stock       edge treat flowering      resi   res_MAD
## 28     31     1 2017      CHATO      IRWIN   211         5 -68.74456 -6.212394
## 32     35     1 2017      CHATO      IRWIN   211        25 -48.74456 -4.405009
## 33     36     1 2017      CHATO      IRWIN   211        25 -48.74456 -4.405009
## 64     67     1 2017      CHATO      CHATO   221        20 -56.92226 -5.144022
## 65     68     1 2017      CHATO      CHATO   221        30 -46.92226 -4.240330
## 67     70     1 2017      CHATO      CHATO   221        15 -61.92226 -5.595869
## 69     72     1 2017      CHATO      CHATO   221        15 -61.92226 -5.595869
## 127   131     2 2017 CHULUCANAS      CHATO   241        25 -58.44225 -5.281382
## 166   171     3 2017 CHULUCANAS CHULUCANAS   141        20 -58.28192 -5.266893
## 213   225     1 2018 CHULUCANAS CHULUCANAS   141        20 -57.68630 -5.213068
## 232   258     1 2018 CHULUCANAS      JULIE   131        10 -56.04249 -5.064518
## 243   273     1 2018 CHULUCANAS      IRWIN   111        15 -58.16121 -5.255986
## 250   284     1 2018      CHATO      CHATO   221        90  47.03144  4.250196
## 251   288     1 2018      CHATO      CHATO   221        97  54.03144  4.882781
## 254   292     2 2018      CHATO      IRWIN   211         5 -63.87075 -5.771952
## 289   328     2 2018      CHATO      CHATO   221         3 -43.83305 -3.961160
## 292   331     2 2018      CHATO      CHATO   221         0 -46.83305 -4.232268
## 298   338     2 2018 CHULUCANAS      IRWIN   111        10 -67.02571 -6.057063
## 299   339     2 2018 CHULUCANAS      IRWIN   111         5 -72.02571 -6.508909
## 311   352     2 2018 CHULUCANAS      JULIE   131        20 -49.90698 -4.510056
## 333   376     3 2018      CHATO      IRWIN   211        20 -44.36027 -4.008804
## 342   385     3 2018 CHULUCANAS CHULUCANAS   141         0 -77.04031 -6.962075
## 346   389     3 2018      CHATO      JULIE   231         0 -47.63760 -4.304974
## 347   390     3 2018      CHATO      JULIE   231         0 -47.63760 -4.304974
## 349   392     3 2018      CHATO      JULIE   231         2 -45.63760 -4.124236
## 375   421     3 2018 CHULUCANAS      JULIE   131        10 -55.39650 -5.006140
## 376   422     3 2018 CHULUCANAS      JULIE   131        20 -45.39650 -4.102448
## 454   505     2 2019      CHATO      IRWIN   211        30 -48.76953 -4.407266
## 526   581     3 2019      CHATO CHULUCANAS   121         7 -70.54614 -6.375202
##              rawp.BHStud                 adjp             bholm out_flag
## 28  0.000000000521833021 0.000000000521833021 0.000000305794150  OUTLIER
## 32  0.000010577939756562 0.000010577939756562 0.006040003600997  OUTLIER
## 33  0.000010577939756562 0.000010577939756562 0.006040003600997  OUTLIER
## 64  0.000000268917649482 0.000000268917649482 0.000155165483751  OUTLIER
## 65  0.000022319169655338 0.000022319169655338 0.012632650024921  OUTLIER
## 67  0.000000021952000884 0.000000021952000884 0.000012798016515  OUTLIER
## 69  0.000000021952000884 0.000000021952000884 0.000012798016515  OUTLIER
## 127 0.000000128212855666 0.000000128212855666 0.000074491669142  OUTLIER
## 166 0.000000138751758749 0.000000138751758749 0.000080476020075  OUTLIER
## 213 0.000000185743196379 0.000000185743196379 0.000107359567507  OUTLIER
## 232 0.000000409435427784 0.000000409435427784 0.000235834806404  OUTLIER
## 243 0.000000147233846226 0.000000147233846226 0.000085248396965  OUTLIER
## 250 0.000021358339519217 0.000021358339519217 0.012110178507396  OUTLIER
## 251 0.000001046000612570 0.000001046000612570 0.000600404351615  OUTLIER
## 254 0.000000007835861071 0.000000007835861071 0.000004576142866  OUTLIER
## 289 0.000074586480278027 0.000074586480278027 0.041843015435973  OUTLIER
## 292 0.000023134658141855 0.000023134658141855 0.013071081850148  OUTLIER
## 298 0.000000001386292858 0.000000001386292858 0.000000810981322  OUTLIER
## 299 0.000000000075698336 0.000000000075698336 0.000000044510622  OUTLIER
## 311 0.000006481038199890 0.000006481038199890 0.003713634888537  OUTLIER
## 333 0.000061026989556012 0.000061026989556012 0.034297168130479  OUTLIER
## 342 0.000000000003352874 0.000000000003352874 0.000000001974843  OUTLIER
## 346 0.000016700498484123 0.000016700498484123 0.009502583637466  OUTLIER
## 347 0.000016700498484123 0.000016700498484123 0.009502583637466  OUTLIER
## 349 0.000037196739853362 0.000037196739853362 0.020978961277296  OUTLIER
## 375 0.000000555322711371 0.000000555322711371 0.000319310559038  OUTLIER
## 376 0.000040880207851313 0.000040880207851313 0.023015557020289  OUTLIER
## 454 0.000010468378508710 0.000010468378508710 0.005987912506982  OUTLIER
## 526 0.000000000182721394 0.000000000182721394 0.000000107257458  OUTLIER

model <- rmout$data$clean %>% 
  aov(formula = lmd, .)

anova(model)
## Analysis of Variance Table
## 
## Response: flowering
##             Df Sum Sq Mean Sq F value            Pr(>F)    
## block        2   1978   988.9  3.3541           0.03566 *  
## year         2  16473  8236.6 27.9371 0.000000000002796 ***
## stock        1    804   803.6  2.7257           0.09932 .  
## edge         3   7276  2425.3  8.2263 0.000023116374039 ***
## stock:edge   3   1580   526.5  1.7859           0.14874    
## Residuals  548 161564   294.8                              
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

mc <- emmeans(model, ~ year|edge|stock) %>%
  cld(Letters = letters, reversed = T) %>%
  mutate(across(.group, trimws)) %>% 
  rename(group = ".group")

mc %>% kable()
year edge stock emmean SE df lower.CL upper.CL group
2 2019 CHATO CHATO 77.01196 2.305001 548 72.48424 81.53968 a
1 2017 CHATO CHATO 76.99530 2.345782 548 72.38748 81.60313 a
3 2018 CHATO CHATO 64.90407 2.425731 548 60.13920 69.66894 b
14 2019 CHATO CHULUCANAS 77.06506 2.198633 548 72.74628 81.38384 a
13 2017 CHATO CHULUCANAS 77.04841 2.208166 548 72.71090 81.38591 a
15 2018 CHATO CHULUCANAS 64.95718 2.269647 548 60.49890 69.41545 b
5 2019 CHULUCANAS CHATO 87.26768 2.233767 548 82.87988 91.65547 a
4 2017 CHULUCANAS CHATO 87.25102 2.226577 548 82.87735 91.62469 a
6 2018 CHULUCANAS CHATO 75.15979 2.289284 548 70.66294 79.65663 b
17 2019 CHULUCANAS CHULUCANAS 85.98612 2.243058 548 81.58008 90.39217 a
16 2017 CHULUCANAS CHULUCANAS 85.96947 2.254128 548 81.54168 90.39726 a
18 2018 CHULUCANAS CHULUCANAS 73.87824 2.329225 548 69.30293 78.45354 b
8 2019 IRWIN CHATO 81.00814 2.291841 548 76.50627 85.51001 a
7 2017 IRWIN CHATO 80.99148 2.311773 548 76.45046 85.53250 a
9 2018 IRWIN CHATO 68.90025 2.375187 548 64.23467 73.56584 b
20 2019 IRWIN CHULUCANAS 86.70568 2.312242 548 82.16374 91.24763 a
19 2017 IRWIN CHULUCANAS 86.68903 2.284768 548 82.20105 91.17700 a
21 2018 IRWIN CHULUCANAS 74.59780 2.389683 548 69.90374 79.29186 b
11 2019 JULIE CHATO 78.22556 2.271401 548 73.76384 82.68728 a
10 2017 JULIE CHATO 78.20890 2.263368 548 73.76296 82.65484 a
12 2018 JULIE CHATO 66.11767 2.367724 548 61.46675 70.76860 b
23 2019 JULIE CHULUCANAS 84.56367 2.325199 548 79.99628 89.13107 a
22 2017 JULIE CHULUCANAS 84.54702 2.315901 548 79.99789 89.09615 a
24 2018 JULIE CHULUCANAS 72.45579 2.429118 548 67.68427 77.22731 b 

p1d <- mc %>% 
  plot_smr(type = "bar"
           , x = "year"
           , y = "emmean"
           , group = "edge"
           , sig = "group"
           , error = "SE"
           , color = T
           , xlab = "Year"
           , ylab = "Flowering ('%')"
           , glab = "Interstock"
           , ylimits = c(0, 120, 20)
           ) +
  facet_wrap(. ~ stock, nrow = 2)

p1d

5.1.5 Figure 3

Univariate analysis of the variables that determine the agronomic characteristics of mango.

legend <- cowplot::get_plot_component(p1a, 'guide-box-top', return_all = TRUE)

p1 <- list(p1a + theme(legend.position="none")
           , p1b + theme(legend.position="none")
           , p1c + theme(legend.position="none")
           , p1d + theme(legend.position="none")
           ) %>% 
  plot_grid(plotlist = ., ncol = 2
            , labels = "auto"
            , rel_heights = c(1, 2)
            ) 

fig <- plot_grid(legend, p1, ncol = 1, align = 'v', rel_heights = c(0.05, 1))

fig %>% 
  ggsave2(plot = ., "submission/Figure_3.jpg"
         , units = "cm"
         , width = 24
         , height = 16
         )

fig %>% 
  ggsave2(plot = ., "submission/Figure_3.eps"
         , units = "cm"
         , width = 24
         , height = 16
         )

knitr::include_graphics("submission/Figure_3.jpg")

5.1.6 Multivariate

Principal Component Analysis (PCA) of agronomic traits in the mango crop based on the use of rootstock-interstock combinations.

mv <- rdt %>% 
  group_by(stock, edge) %>% 
  summarise(across(where(is.numeric), ~ mean(., na.rm = T))) %>%   
  unite("treat", stock:edge, sep = "-") %>% 
   rename(Treat = treat
         , Height = height
         , Fruits = n_fruits
         , Flowering = flowering
         , Sproud = sproud)
  
pca <- mv %>% 
  PCA(scale.unit = T, quali.sup = 1, graph = F) 

# summary

summary(pca, nbelements = Inf, nb.dec = 2)
## 
## Call:
## PCA(X = ., scale.unit = T, quali.sup = 1, graph = F) 
## 
## 
## Eigenvalues
##                       Dim.1  Dim.2  Dim.3  Dim.4
## Variance               2.49   0.93   0.37   0.21
## % of var.             62.27  23.26   9.22   5.26
## Cumulative % of var.  62.27  85.52  94.74 100.00
## 
## Individuals
##                          Dist   Dim.1   ctr  cos2   Dim.2   ctr  cos2   Dim.3
## 1                     |  2.28 | -1.99 19.84  0.76 | -0.72  7.04  0.10 | -0.43
## 2                     |  1.67 |  1.05  5.51  0.39 | -0.61  4.97  0.13 |  1.15
## 3                     |  2.18 | -1.90 18.19  0.76 |  0.89 10.71  0.17 |  0.36
## 4                     |  1.74 | -1.44 10.38  0.68 |  0.94 11.90  0.29 | -0.19
## 5                     |  1.52 | -0.49  1.23  0.11 | -1.05 14.72  0.47 | -0.41
## 6                     |  2.83 |  2.26 25.69  0.64 |  1.63 35.67  0.33 | -0.51
## 7                     |  1.00 |  0.68  2.34  0.46 | -0.03  0.01  0.00 |  0.67
## 8                     |  2.21 |  1.83 16.82  0.69 | -1.06 14.98  0.23 | -0.63
##                         ctr  cos2  
## 1                      6.36  0.04 |
## 2                     44.91  0.47 |
## 3                      4.40  0.03 |
## 4                      1.21  0.01 |
## 5                      5.78  0.07 |
## 6                      8.90  0.03 |
## 7                     15.00  0.44 |
## 8                     13.43  0.08 |
## 
## Variables
##                         Dim.1   ctr  cos2   Dim.2   ctr  cos2   Dim.3   ctr
## Height                | -0.91 33.26  0.83 |  0.07  0.53  0.00 |  0.25 17.31
## Fruits                |  0.57 13.26  0.33 |  0.78 66.19  0.62 | -0.16  6.81
## Flowering             |  0.86 29.71  0.74 |  0.05  0.23  0.00 |  0.51 69.79
## Sproud                |  0.77 23.77  0.59 | -0.55 33.05  0.31 | -0.15  6.10
##                        cos2  
## Height                 0.06 |
## Fruits                 0.03 |
## Flowering              0.26 |
## Sproud                 0.02 |
## 
## Supplementary categories
##                          Dist   Dim.1  cos2 v.test   Dim.2  cos2 v.test   Dim.3
## CHATO-CHATO           |  2.28 | -1.99  0.76  -1.26 | -0.72  0.10  -0.75 | -0.43
## CHATO-CHULUCANAS      |  1.67 |  1.05  0.39   0.66 | -0.61  0.13  -0.63 |  1.15
## CHATO-IRWIN           |  2.18 | -1.90  0.76  -1.21 |  0.89  0.17   0.93 |  0.36
## CHATO-JULIE           |  1.74 | -1.44  0.68  -0.91 |  0.94  0.29   0.98 | -0.19
## CHULUCANAS-CHATO      |  1.52 | -0.49  0.11  -0.31 | -1.05  0.47  -1.09 | -0.41
## CHULUCANAS-CHULUCANAS |  2.83 |  2.26  0.64   1.43 |  1.63  0.33   1.69 | -0.51
## CHULUCANAS-IRWIN      |  1.00 |  0.68  0.46   0.43 | -0.03  0.00  -0.03 |  0.67
## CHULUCANAS-JULIE      |  2.21 |  1.83  0.69   1.16 | -1.06  0.23  -1.09 | -0.63
##                        cos2 v.test  
## CHATO-CHATO            0.04  -0.71 |
## CHATO-CHULUCANAS       0.47   1.90 |
## CHATO-IRWIN            0.03   0.59 |
## CHATO-JULIE            0.01  -0.31 |
## CHULUCANAS-CHATO       0.07  -0.68 |
## CHULUCANAS-CHULUCANAS  0.03  -0.84 |
## CHULUCANAS-IRWIN       0.44   1.10 |
## CHULUCANAS-JULIE       0.08  -1.04 |

f2a <- plot.PCA(x = pca, choix = "var"
                , cex=0.8
                , label = "var"
                )

f2b <- plot.PCA(x = pca, choix = "ind"
                , habillage = 1
                , invisible = c("ind")
                , cex=0.8
                )

5.1.7 Figure 4

Principal Component Analysis (PCA).

fig <- list(f2a, f2b) %>% 
  plot_grid(plotlist = ., ncol = 2, nrow = 1
            , labels = "auto"
            , rel_widths = c(1, 1.5)
            ) 
fig %>% 
  ggsave2(plot = ., "submission/Figure_4.jpg", units = "cm"
          , width = 25, height = 10
          ) 

fig %>% 
  ggsave2(plot = ., "submission/Figure_4.eps", units = "cm"
          , width = 25, height = 10
          ) 

knitr::include_graphics("submission/Figure_4.jpg")

5.1.8 Supplementary Figure 1

Results of the contributions and correlation of the variables in the Principal Component Analysis (PCA).

var <- get_pca_var(pca)

pt1 <- fviz_eig(pca, 
                addlabels=TRUE,
                hjust = 0.05,
                barfill="white",
                barcolor ="darkblue",
                linecolor ="red") + 
  ylim(0, 80) + 
  labs(
    title = "PCA - percentage of explained variances",
    y = "Variance (%)") +
  theme_minimal()

pt2 <- fviz_contrib(pca,
                     choice = "var", 
                     axes = 1, 
                     top = 10,
                     fill="white",
                     color ="darkblue",
                     sort.val = "desc") +
  ylim(0, 50) + 
  labs(title = "Dim 1 - variables contribution") 

pt3 <- fviz_contrib(pca,
                     choice = "var", 
                     axes = 2, 
                     top = 10,
                     fill="white",
                     color ="darkblue",
                     sort.val = "desc") +
  ylim(0, 80) + 
  labs(title = "Dim 2 - variables contribution") 

pt4 <- ~ {
  
  corrplot(var$cor, 
         method="number",
         tl.col="black", 
         tl.srt=45,
         )
  
}

plot <- list(pt1, pt2, pt3) %>% 
  plot_grid(plotlist = ., ncol = 1, labels = "auto") %>% 
  list(., pt4) %>% 
  plot_grid(plotlist = ., ncol = 2, labels = c("", "d"))


ggsave2(plot = plot, "submission/FigS1.jpg", height = 20, width = 28, units = "cm")

ggsave2(plot = plot, "submission/FigS1.eps", height = 20, width = 28, units = "cm")

knitr::include_graphics("submission/FigS1.jpg")

5.2 Specific Objective 2

Determine the effect of the rootstock-interstock interaction on the fruit biometrics of mango.

5.2.1 Fruit Weigth

trait <- "weigth"

lmm <- paste({{trait}}, "~ 1 + (1|block) + stock*edge") %>% as.formula()

lmd <- paste({{trait}}, "~ block + stock*edge") %>% as.formula()

rmout <- fru %>% 
  remove_outliers(formula = lmm
                  , drop_na = T, plot_diag = T)

rmout$diagplot


rmout$outliers
##  [1] index       block       stock       edge        weigth      resi       
##  [7] res_MAD     rawp.BHStud adjp        bholm       out_flag   
## <0 rows> (o 0- extensión row.names)

model <- rmout$data$clean %>% 
  aov(formula = lmd, .)

anova(model)
## Analysis of Variance Table
## 
## Response: weigth
##             Df  Sum Sq Mean Sq F value  Pr(>F)  
## block        2   73498   36749  4.6207 0.01078 *
## stock        1    1816    1816  0.2284 0.63320  
## edge         3    4318    1439  0.1810 0.90923  
## stock:edge   3   37870   12623  1.5872 0.19323  
## Residuals  229 1821257    7953                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

mc <- emmeans(model, ~ edge|stock) %>%
  cld(Letters = letters, reversed = T) %>%
  mutate(across(.group, trimws)) %>% 
  rename(group = ".group")

mc %>% kable()
edge stock emmean SE df lower.CL upper.CL group
1 IRWIN CHATO 482.0333 16.28198 229 449.9517 514.1150 a
2 JULIE CHATO 465.9667 16.28198 229 433.8850 498.0483 a
4 CHULUCANAS CHATO 462.3661 16.56272 229 429.7313 495.0009 a
3 CHATO CHATO 452.5333 16.28198 229 420.4517 484.6150 a
7 CHATO CHULUCANAS 484.9667 16.28198 229 452.8850 517.0483 a
6 JULIE CHULUCANAS 484.7000 16.28198 229 452.6184 516.7816 a
8 CHULUCANAS CHULUCANAS 468.1667 16.28198 229 436.0850 500.2483 a
5 IRWIN CHULUCANAS 447.2333 16.28198 229 415.1517 479.3150 a 

p2a <- mc %>% 
  plot_smr(x = "stock"
           , y = "emmean"
           , group = "edge"
           , sig = "group"
           , error = "SE"
           , color = T
           , xlab = "Rootstock"
           , ylab = "Fruit Weigth (g)"
           , glab = "Interstock"
           , ylimits = c(0, 600, 100)
           , 
           )

p2a

5.2.2 Fruit length

trait <- "long"

lmm <- paste({{trait}}, "~ 1 + (1|block) + stock*edge") %>% as.formula()

lmd <- paste({{trait}}, "~ block + stock*edge") %>% as.formula()

rmout <- fru %>% 
  remove_outliers(formula = lmm
                  , drop_na = T, plot_diag = T)

rmout$diagplot


rmout$outliers
##  [1] index       block       stock       edge        long        resi       
##  [7] res_MAD     rawp.BHStud adjp        bholm       out_flag   
## <0 rows> (o 0- extensión row.names)

model <- rmout$data$clean %>% 
  aov(formula = lmd, .)

anova(model)
## Analysis of Variance Table
## 
## Response: long
##             Df  Sum Sq Mean Sq F value   Pr(>F)   
## block        2   681.4  340.70  5.9312 0.003081 **
## stock        1    31.2   31.16  0.5424 0.462192   
## edge         3   270.1   90.02  1.5672 0.198107   
## stock:edge   3    39.5   13.15  0.2289 0.876184   
## Residuals  229 13154.4   57.44                    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

mc <- emmeans(model, ~ edge|stock) %>%
  cld(Letters = letters, reversed = T) %>%
  mutate(across(.group, trimws)) %>% 
  rename(group = ".group")

mc %>% kable()
edge stock emmean SE df lower.CL upper.CL group
1 JULIE CHATO 107.6667 1.383746 229 104.9402 110.3932 a
3 CHULUCANAS CHATO 106.3048 1.407606 229 103.5313 109.0783 a
2 IRWIN CHATO 106.0667 1.383746 229 103.3402 108.7932 a
4 CHATO CHATO 105.9667 1.383746 229 103.2402 108.6932 a
6 JULIE CHULUCANAS 109.7333 1.383746 229 107.0068 112.4598 a
7 CHATO CHULUCANAS 106.5333 1.383746 229 103.8068 109.2598 a
5 IRWIN CHULUCANAS 106.4333 1.383746 229 103.7068 109.1598 a
8 CHULUCANAS CHULUCANAS 106.2000 1.383746 229 103.4735 108.9265 a 

p2b <- mc %>% 
  plot_smr(x = "stock"
           , y = "emmean"
           , group = "edge"
           , sig = "group"
           , error = "SE"
           , color = T
           , xlab = "Rootstock"
           , ylab = "Fruit length (mm)"
           , glab = "Interstock"
           , ylimits = c(0, 120, 20)
           , 
           )

p2b

5.2.3 Fruit diameter

trait <- "diameter_average"

lmm <- paste({{trait}}, "~ 1 + (1|block) + stock*edge") %>% as.formula()

lmd <- paste({{trait}}, "~ block + stock*edge") %>% as.formula()

rmout <- fru %>% 
  remove_outliers(formula = lmm
                  , drop_na = T, plot_diag = T)

rmout$diagplot


rmout$outliers
##  [1] index            block            stock            edge            
##  [5] diameter_average resi             res_MAD          rawp.BHStud     
##  [9] adjp             bholm            out_flag        
## <0 rows> (o 0- extensión row.names)

model <- rmout$data$clean %>% 
  aov(formula = lmd, .)

anova(model)
## Analysis of Variance Table
## 
## Response: diameter_average
##             Df Sum Sq Mean Sq F value   Pr(>F)   
## block        2  248.1 124.033  5.0489 0.007149 **
## stock        1    1.2   1.178  0.0480 0.826864   
## edge         3    6.1   2.027  0.0825 0.969504   
## stock:edge   3  234.3  78.109  3.1796 0.024804 * 
## Residuals  229 5625.6  24.566                    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

mc <- emmeans(model, ~ edge|stock) %>%
  cld(Letters = letters, reversed = T) %>%
  mutate(across(.group, trimws)) %>% 
  rename(group = ".group")

mc %>% kable()
edge stock emmean SE df lower.CL upper.CL group
1 IRWIN CHATO 87.63333 0.9049151 229 85.85031 89.41636 a
2 JULIE CHATO 86.23333 0.9049151 229 84.45031 88.01636 a
4 CHULUCANAS CHATO 86.07080 0.9205182 229 84.25703 87.88457 a
3 CHATO CHATO 85.23333 0.9049151 229 83.45031 87.01636 a
7 CHATO CHULUCANAS 87.70000 0.9049151 229 85.91698 89.48302 a
6 JULIE CHULUCANAS 86.30000 0.9049151 229 84.51698 88.08302 a
8 CHULUCANAS CHULUCANAS 86.08333 0.9049151 229 84.30031 87.86636 a
5 IRWIN CHULUCANAS 84.53333 0.9049151 229 82.75031 86.31636 a 

p2c <- mc %>% 
  plot_smr(x = "stock"
           , y = "emmean"
           , group = "edge"
           , sig = "group"
           , error = "SE"
           , color = T
           , xlab = "Rootstock"
           , ylab = "Fruit diameter (mm)"
           , glab = "Interstock"
           , ylimits = c(0, 100, 20)
           , 
           )

p2c

5.2.4 Table 2

Descriptive statistics of the variables that determine the fruit biometrics of mango.


sts <- Summarize(weigth  ~ stock*edge, data = fru, digits = 2, na.rm = TRUE)

tb1a <- sts%>% 
  merge(., mc) %>% 
  mutate(Variable = "Fruit Weigth (g)") %>% 
  dplyr::select(Variable, stock, edge, mean, sd, min, max, group) %>% 
  rename(Rootstock = stock,
         Interstock = edge,
         Sig = group)

sts <- Summarize(long  ~ stock*edge, data = fru, digits = 2, na.rm = TRUE)

tb1b <- sts%>% 
  merge(., mc) %>% 
  mutate(Variable = "Fruit length (mm)") %>% 
  dplyr::select(Variable, stock, edge, mean, sd, min, max, group) %>% 
  rename(Rootstock = stock,
         Interstock = edge,
         Sig = group)

sts <- Summarize(diameter_average ~ stock*edge, data = fru, digits = 2, na.rm = TRUE)

tb1c <- sts%>% 
  merge(., mc) %>% 
  mutate(Variable = "Fruit diameter (mm)") %>% 
  dplyr::select(Variable, stock, edge, mean, sd, min, max, group) %>% 
  rename(Rootstock = stock,
         Interstock = edge,
         Sig = group)

tb1 <- bind_rows(tb1a, tb1b, tb1c)

tb1 %>% kable(align = 'c')
Variable Rootstock Interstock mean sd min max Sig
Fruit Weigth (g) CHATO CHATO 452.53 79.92 305.0 639.0 a
Fruit Weigth (g) CHATO CHULUCANAS 462.34 69.82 340.0 620.0 a
Fruit Weigth (g) CHATO IRWIN 482.03 87.30 350.0 700.0 a
Fruit Weigth (g) CHATO JULIE 465.97 92.07 300.0 645.0 a
Fruit Weigth (g) CHULUCANAS CHATO 484.97 101.98 316.0 765.0 a
Fruit Weigth (g) CHULUCANAS CHULUCANAS 468.17 118.73 230.0 665.0 a
Fruit Weigth (g) CHULUCANAS IRWIN 447.23 70.09 310.0 605.0 a
Fruit Weigth (g) CHULUCANAS JULIE 484.70 93.46 367.0 717.0 a
Fruit length (mm) CHATO CHATO 105.97 6.78 93.0 120.0 a
Fruit length (mm) CHATO CHULUCANAS 106.28 6.80 94.0 119.0 a
Fruit length (mm) CHATO IRWIN 106.07 6.47 95.0 124.0 a
Fruit length (mm) CHATO JULIE 107.67 7.54 91.0 120.0 a
Fruit length (mm) CHULUCANAS CHATO 106.53 7.21 95.0 123.0 a
Fruit length (mm) CHULUCANAS CHULUCANAS 106.20 10.87 86.0 126.0 a
Fruit length (mm) CHULUCANAS IRWIN 106.43 7.28 92.0 122.0 a
Fruit length (mm) CHULUCANAS JULIE 109.73 8.03 98.0 129.0 a
Fruit diameter (mm) CHATO CHATO 85.23 4.72 76.0 97.5 a
Fruit diameter (mm) CHATO CHULUCANAS 86.09 3.61 78.5 93.5 a
Fruit diameter (mm) CHATO IRWIN 87.63 5.07 79.5 100.0 a
Fruit diameter (mm) CHATO JULIE 86.23 5.56 75.5 97.5 a
Fruit diameter (mm) CHULUCANAS CHATO 87.70 5.40 78.0 100.5 a
Fruit diameter (mm) CHULUCANAS CHULUCANAS 86.08 6.61 69.5 99.0 a
Fruit diameter (mm) CHULUCANAS IRWIN 84.53 4.03 77.5 92.5 a
Fruit diameter (mm) CHULUCANAS JULIE 86.30 4.68 79.5 97.0 a 

tb1 %>%
  write_sheet(ss = gs, sheet = "tb1")

5.2.5 Multivariate

Principal Component Analysis (PCA) of fruit biometrics in the mango crop based on the use of rootstock-interstock combinations.

mv <- fru %>% 
  group_by(stock, edge) %>% 
  summarise(across(where(is.numeric), ~ mean(., na.rm = T))) %>%
  dplyr::select(!c(diameter_1, diameter_2, n_fruits)) %>%
  unite("treat", stock:edge, sep = "-") %>% 
   rename(Treat = treat
         , Weight = weigth
         , Length = long
         , Diameter = diameter_average)
  
pca <- mv %>% 
  PCA(scale.unit = T, quali.sup = 1, graph = F) 

# summary

summary(pca, nbelements = Inf, nb.dec = 2)
## 
## Call:
## PCA(X = ., scale.unit = T, quali.sup = 1, graph = F) 
## 
## 
## Eigenvalues
##                       Dim.1  Dim.2  Dim.3
## Variance               2.01   0.97   0.02
## % of var.             66.97  32.23   0.79
## Cumulative % of var.  66.97  99.21 100.00
## 
## Individuals
##                          Dist   Dim.1   ctr  cos2   Dim.2   ctr  cos2   Dim.3
## 1                     |  1.71 | -1.70 17.91  0.98 | -0.22  0.60  0.02 | -0.01
## 2                     |  0.68 | -0.56  1.99  0.69 | -0.37  1.76  0.29 |  0.09
## 3                     |  1.85 |  1.38 11.78  0.56 | -1.23 19.60  0.44 |  0.00
## 4                     |  0.70 |  0.09  0.05  0.02 |  0.61  4.83  0.76 |  0.33
## 5                     |  1.93 |  1.70 17.92  0.78 | -0.91 10.79  0.22 |  0.00
## 6                     |  0.57 | -0.29  0.51  0.25 | -0.44  2.46  0.58 | -0.23
## 7                     |  2.33 | -2.29 32.54  0.96 |  0.45  2.60  0.04 | -0.06
## 8                     |  2.69 |  1.67 17.30  0.38 |  2.11 57.36  0.61 | -0.12
##                         ctr  cos2  
## 1                      0.03  0.00 |
## 2                      4.74  0.02 |
## 3                      0.00  0.00 |
## 4                     57.67  0.22 |
## 5                      0.00  0.00 |
## 6                     28.15  0.16 |
## 7                      2.18  0.00 |
## 8                      7.23  0.00 |
## 
## Variables
##                         Dim.1   ctr  cos2   Dim.2   ctr  cos2   Dim.3   ctr
## Weight                |  0.99 49.14  0.99 | -0.03  0.07  0.00 | -0.11 50.79
## Length                |  0.46 10.55  0.21 |  0.89 81.29  0.79 |  0.04  8.16
## Diameter              |  0.90 40.32  0.81 | -0.42 18.64  0.18 |  0.10 41.05
##                        cos2  
## Weight                 0.01 |
## Length                 0.00 |
## Diameter               0.01 |
## 
## Supplementary categories
##                          Dist   Dim.1  cos2 v.test   Dim.2  cos2 v.test   Dim.3
## CHATO-CHATO           |  1.71 | -1.70  0.98  -1.20 | -0.22  0.02  -0.22 | -0.01
## CHATO-CHULUCANAS      |  0.68 | -0.56  0.69  -0.40 | -0.37  0.29  -0.38 |  0.09
## CHATO-IRWIN           |  1.85 |  1.38  0.56   0.97 | -1.23  0.44  -1.25 |  0.00
## CHATO-JULIE           |  0.70 |  0.09  0.02   0.07 |  0.61  0.76   0.62 |  0.33
## CHULUCANAS-CHATO      |  1.93 |  1.70  0.78   1.20 | -0.91  0.22  -0.93 |  0.00
## CHULUCANAS-CHULUCANAS |  0.57 | -0.29  0.25  -0.20 | -0.44  0.58  -0.44 | -0.23
## CHULUCANAS-IRWIN      |  2.33 | -2.29  0.96  -1.61 |  0.45  0.04   0.46 | -0.06
## CHULUCANAS-JULIE      |  2.69 |  1.67  0.38   1.18 |  2.11  0.61   2.14 | -0.12
##                        cos2 v.test  
## CHATO-CHATO            0.00  -0.05 |
## CHATO-CHULUCANAS       0.02   0.62 |
## CHATO-IRWIN            0.00  -0.02 |
## CHATO-JULIE            0.22   2.15 |
## CHULUCANAS-CHATO       0.00  -0.02 |
## CHULUCANAS-CHULUCANAS  0.16  -1.50 |
## CHULUCANAS-IRWIN       0.00  -0.42 |
## CHULUCANAS-JULIE       0.00  -0.76 |

f3a <- plot.PCA(x = pca, choix = "var"
                , cex=0.8
                )

f3b <- plot.PCA(x = pca, choix = "ind"
                , habillage = 1
                , invisible = c("ind")
                , cex=0.8
                )

5.2.6 Figure 5

Principal Component Analysis (PCA).

fig <- list(f3a, f3b) %>% 
  plot_grid(plotlist = ., ncol = 2, nrow = 1
            , labels = "auto"
            , rel_widths = c(1, 1.5)
            ) 
fig %>% 
  ggsave2(plot = ., "submission/Figure_5.jpg", units = "cm"
          , width = 25, height = 10
          ) 

fig %>% 
  ggsave2(plot = ., "submission/Figure_5.eps", units = "cm"
          , width = 25, height = 10
          ) 

knitr::include_graphics("submission/Figure_5.jpg")

5.2.7 Supplementary Figure 2

Results of the contributions and correlation of the variables in the Principal Component Analysis (PCA).

var <- get_pca_var(pca)

pt1 <- fviz_eig(pca, 
                addlabels=TRUE,
                hjust = 0.05,
                barfill="white",
                barcolor ="darkblue",
                linecolor = "white") + 
  ylim(0, 80) + 
  labs(
    title = "PCA - percentage of explained variances",
    y = "Variance (%)") +
  theme_minimal()

pt2 <- fviz_contrib(pca,
                     choice = "var", 
                     axes = 1, 
                     top = 10,
                     fill="white",
                     color ="darkblue",
                     sort.val = "desc") +
  ylim(0, 60) + 
  labs(title = "Dim 1 - variables contribution") 

pt3 <- fviz_contrib(pca,
                     choice = "var", 
                     axes = 2, 
                     top = 10,
                     fill="white",
                     color ="darkblue",
                     sort.val = "desc") +
  ylim(0, 100) + 
  labs(title = "Dim 2 - variables contribution") 

pt4 <- ~ {
  
  corrplot(var$cor, 
         method="number",
         tl.col="black", 
         tl.srt=45,
         )
  
}

plot <- list(pt1, pt2, pt3) %>% 
  plot_grid(plotlist = ., ncol = 1, labels = "auto") %>% 
  list(., pt4) %>% 
  plot_grid(plotlist = ., ncol = 2, labels = c("", "d"))


ggsave2(plot = plot, "submission/FigS2.jpg", height = 20, width = 30, units = "cm")

ggsave2(plot = plot, "submission/FigS2.eps", height = 20, width = 30, units = "cm")

knitr::include_graphics("submission/FigS2.jpg")